{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use `Folder` for Customs Datasets\n",
    "\n",
    "# Installing Anomalib\n",
    "\n",
    "The easiest way to install anomalib is to use pip. You can install it from the command line using the following command:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install anomalib"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up the Dataset Directory\n",
    "\n",
    "This cell is to ensure we change the directory to have access to the datasets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# NOTE: Provide the path to the dataset root directory.\n",
    "#   If the datasets is not downloaded, it will be downloaded\n",
    "#   to this directory.\n",
    "dataset_root = Path.cwd().parent / \"datasets\" / \"hazelnut_toy\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use Folder Dataset (for Custom Datasets) via API\n",
    "\n",
    "Here we show how one can utilize custom datasets to train anomalib models. A custom dataset in this model can be of the following types:\n",
    "\n",
    "- A dataset with good and bad images.\n",
    "- A dataset with good and bad images as well as mask ground-truths for pixel-wise evaluation.\n",
    "- A dataset with good and bad images that is already split into training and testing sets.\n",
    "\n",
    "To experiment this setting we provide a toy dataset that could be downloaded from the following [https://github.com/openvinotoolkit/anomalib/blob/main/docs/source/data/hazelnut_toy.zip](link). For the rest of the tutorial, we assume that the dataset is downloaded and extracted to `../datasets`, located in the `anomalib` directory.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flake8: noqa\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from torchvision.transforms.v2 import Resize\n",
    "from torchvision.transforms.v2.functional import to_pil_image\n",
    "\n",
    "from anomalib.data.image.folder import Folder, FolderDataset\n",
    "from anomalib import TaskType"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataModule\n",
    "\n",
    "Similar to how we created the datamodules for existing benchmarking datasets in the previous tutorials, we can also create an Anomalib datamodule for our custom hazelnut dataset.\n",
    "\n",
    "In addition to the root folder of the dataset, we now also specify which folder contains the normal images, which folder contains the anomalous images, and which folder contains the ground truth masks for the anomalous images.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_datamodule = Folder(\n",
    "    name=\"hazelnut_toy\",\n",
    "    root=dataset_root,\n",
    "    normal_dir=\"good\",\n",
    "    abnormal_dir=\"crack\",\n",
    "    task=TaskType.SEGMENTATION,\n",
    "    mask_dir=dataset_root / \"mask\" / \"crack\",\n",
    "    image_size=(256, 256),\n",
    ")\n",
    "folder_datamodule.setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['image_path', 'label', 'image', 'mask']) torch.Size([28, 3, 256, 256])\n"
     ]
    }
   ],
   "source": [
    "# Train images\n",
    "i, data = next(enumerate(folder_datamodule.train_dataloader()))\n",
    "print(data.keys(), data[\"image\"].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['image_path', 'label', 'image', 'mask']) torch.Size([6, 3, 256, 256]) torch.Size([6, 256, 256])\n"
     ]
    }
   ],
   "source": [
    "# Test images\n",
    "i, data = next(enumerate(folder_datamodule.test_dataloader()))\n",
    "print(data.keys(), data[\"image\"].shape, data[\"mask\"].shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen above, creating the dataloaders are pretty straghtforward, which could be directly used for training/testing/inference. We could visualize samples from the dataloaders as well.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=512x256>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = to_pil_image(data[\"image\"][0].clone())\n",
    "msk = to_pil_image(data[\"mask\"][0]).convert(\"RGB\")\n",
    "\n",
    "Image.fromarray(np.hstack((np.array(img), np.array(msk))))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Folder` data module offers much more flexibility cater all different sorts of needs. Please refer to the documentation for more details.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Torch Dataset\n",
    "\n",
    "As in earlier examples, we can also create a standalone PyTorch dataset instance.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "FolderDataset??"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add some transforms that will be applied to the images using torchvision. Let's add a transform that resizes the \n",
    "input image to 256x256 pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_size = (256, 256)\n",
    "transform = Resize(image_size, antialias=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classification Task\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>label</th>\n",
       "      <th>label_index</th>\n",
       "      <th>mask_path</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/good/00.jpg</td>\n",
       "      <td>DirType.NORMAL</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Split.TRAIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/good/01.jpg</td>\n",
       "      <td>DirType.NORMAL</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Split.TRAIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/good/02.jpg</td>\n",
       "      <td>DirType.NORMAL</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Split.TRAIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/good/03.jpg</td>\n",
       "      <td>DirType.NORMAL</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Split.TRAIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/good/04.jpg</td>\n",
       "      <td>DirType.NORMAL</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Split.TRAIN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        image_path           label  \\\n",
       "0  /home/djameln/datasets/hazelnut_toy/good/00.jpg  DirType.NORMAL   \n",
       "1  /home/djameln/datasets/hazelnut_toy/good/01.jpg  DirType.NORMAL   \n",
       "2  /home/djameln/datasets/hazelnut_toy/good/02.jpg  DirType.NORMAL   \n",
       "3  /home/djameln/datasets/hazelnut_toy/good/03.jpg  DirType.NORMAL   \n",
       "4  /home/djameln/datasets/hazelnut_toy/good/04.jpg  DirType.NORMAL   \n",
       "\n",
       "   label_index mask_path        split  \n",
       "0            0            Split.TRAIN  \n",
       "1            0            Split.TRAIN  \n",
       "2            0            Split.TRAIN  \n",
       "3            0            Split.TRAIN  \n",
       "4            0            Split.TRAIN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folder_dataset_classification_train = FolderDataset(\n",
    "    name=\"hazelnut_toy\",\n",
    "    normal_dir=dataset_root / \"good\",\n",
    "    abnormal_dir=dataset_root / \"crack\",\n",
    "    split=\"train\",\n",
    "    transform=transform,\n",
    "    task=TaskType.CLASSIFICATION,\n",
    ")\n",
    "folder_dataset_classification_train.samples.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the first sample in the dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['image_path', 'label', 'image']) torch.Size([3, 256, 256])\n"
     ]
    }
   ],
   "source": [
    "data = folder_dataset_classification_train[0]\n",
    "print(data.keys(), data[\"image\"].shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen above, when we choose `classification` task and `train` split, the dataset only returns `image`. This is mainly because training only requires normal images and no labels. Now let's try `test` split for the `classification` task\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>label</th>\n",
       "      <th>label_index</th>\n",
       "      <th>mask_path</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/crack/01.jpg</td>\n",
       "      <td>DirType.ABNORMAL</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>Split.TEST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/crack/02.jpg</td>\n",
       "      <td>DirType.ABNORMAL</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>Split.TEST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/crack/03.jpg</td>\n",
       "      <td>DirType.ABNORMAL</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>Split.TEST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/crack/04.jpg</td>\n",
       "      <td>DirType.ABNORMAL</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>Split.TEST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/crack/05.jpg</td>\n",
       "      <td>DirType.ABNORMAL</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>Split.TEST</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         image_path             label  \\\n",
       "0  /home/djameln/datasets/hazelnut_toy/crack/01.jpg  DirType.ABNORMAL   \n",
       "1  /home/djameln/datasets/hazelnut_toy/crack/02.jpg  DirType.ABNORMAL   \n",
       "2  /home/djameln/datasets/hazelnut_toy/crack/03.jpg  DirType.ABNORMAL   \n",
       "3  /home/djameln/datasets/hazelnut_toy/crack/04.jpg  DirType.ABNORMAL   \n",
       "4  /home/djameln/datasets/hazelnut_toy/crack/05.jpg  DirType.ABNORMAL   \n",
       "\n",
       "   label_index mask_path       split  \n",
       "0            1            Split.TEST  \n",
       "1            1            Split.TEST  \n",
       "2            1            Split.TEST  \n",
       "3            1            Split.TEST  \n",
       "4            1            Split.TEST  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Folder Classification Test Set\n",
    "folder_dataset_classification_test = FolderDataset(\n",
    "    name=\"hazelnut_toy\",\n",
    "    normal_dir=dataset_root / \"good\",\n",
    "    abnormal_dir=dataset_root / \"crack\",\n",
    "    split=\"test\",\n",
    "    transform=transform,\n",
    "    task=TaskType.CLASSIFICATION,\n",
    ")\n",
    "folder_dataset_classification_test.samples.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['image_path', 'label', 'image']) torch.Size([3, 256, 256]) /home/djameln/datasets/hazelnut_toy/crack/01.jpg 1\n"
     ]
    }
   ],
   "source": [
    "data = folder_dataset_classification_test[0]\n",
    "print(data.keys(), data[\"image\"].shape, data[\"image_path\"], data[\"label\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Segmentation Task\n",
    "\n",
    "It is also possible to configure the Folder dataset for the segmentation task, where the dataset object returns image and ground-truth mask.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>label</th>\n",
       "      <th>label_index</th>\n",
       "      <th>mask_path</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/good/00.jpg</td>\n",
       "      <td>DirType.NORMAL</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Split.TRAIN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/good/01.jpg</td>\n",
       "      <td>DirType.NORMAL</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Split.TRAIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/good/02.jpg</td>\n",
       "      <td>DirType.NORMAL</td>\n",
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       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/good/03.jpg</td>\n",
       "      <td>DirType.NORMAL</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Split.TRAIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/good/04.jpg</td>\n",
       "      <td>DirType.NORMAL</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Split.TRAIN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        image_path           label  \\\n",
       "0  /home/djameln/datasets/hazelnut_toy/good/00.jpg  DirType.NORMAL   \n",
       "1  /home/djameln/datasets/hazelnut_toy/good/01.jpg  DirType.NORMAL   \n",
       "2  /home/djameln/datasets/hazelnut_toy/good/02.jpg  DirType.NORMAL   \n",
       "3  /home/djameln/datasets/hazelnut_toy/good/03.jpg  DirType.NORMAL   \n",
       "4  /home/djameln/datasets/hazelnut_toy/good/04.jpg  DirType.NORMAL   \n",
       "\n",
       "   label_index mask_path        split  \n",
       "0            0            Split.TRAIN  \n",
       "1            0            Split.TRAIN  \n",
       "2            0            Split.TRAIN  \n",
       "3            0            Split.TRAIN  \n",
       "4            0            Split.TRAIN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Folder Segmentation Train Set\n",
    "folder_dataset_segmentation_train = FolderDataset(\n",
    "    name=\"hazelnut_toy\",\n",
    "    normal_dir=dataset_root / \"good\",\n",
    "    abnormal_dir=dataset_root / \"crack\",\n",
    "    split=\"train\",\n",
    "    transform=transform,\n",
    "    mask_dir=dataset_root / \"mask\" / \"crack\",\n",
    "    task=TaskType.SEGMENTATION,\n",
    ")\n",
    "folder_dataset_segmentation_train.samples.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>label</th>\n",
       "      <th>label_index</th>\n",
       "      <th>mask_path</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/crack/01.jpg</td>\n",
       "      <td>DirType.ABNORMAL</td>\n",
       "      <td>1</td>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/mask/crack...</td>\n",
       "      <td>Split.TEST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/crack/02.jpg</td>\n",
       "      <td>DirType.ABNORMAL</td>\n",
       "      <td>1</td>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/mask/crack...</td>\n",
       "      <td>Split.TEST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/crack/03.jpg</td>\n",
       "      <td>DirType.ABNORMAL</td>\n",
       "      <td>1</td>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/mask/crack...</td>\n",
       "      <td>Split.TEST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/crack/04.jpg</td>\n",
       "      <td>DirType.ABNORMAL</td>\n",
       "      <td>1</td>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/mask/crack...</td>\n",
       "      <td>Split.TEST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/crack/05.jpg</td>\n",
       "      <td>DirType.ABNORMAL</td>\n",
       "      <td>1</td>\n",
       "      <td>/home/djameln/datasets/hazelnut_toy/mask/crack...</td>\n",
       "      <td>Split.TEST</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         image_path             label  \\\n",
       "0  /home/djameln/datasets/hazelnut_toy/crack/01.jpg  DirType.ABNORMAL   \n",
       "1  /home/djameln/datasets/hazelnut_toy/crack/02.jpg  DirType.ABNORMAL   \n",
       "2  /home/djameln/datasets/hazelnut_toy/crack/03.jpg  DirType.ABNORMAL   \n",
       "3  /home/djameln/datasets/hazelnut_toy/crack/04.jpg  DirType.ABNORMAL   \n",
       "4  /home/djameln/datasets/hazelnut_toy/crack/05.jpg  DirType.ABNORMAL   \n",
       "\n",
       "   label_index                                          mask_path       split  \n",
       "0            1  /home/djameln/datasets/hazelnut_toy/mask/crack...  Split.TEST  \n",
       "1            1  /home/djameln/datasets/hazelnut_toy/mask/crack...  Split.TEST  \n",
       "2            1  /home/djameln/datasets/hazelnut_toy/mask/crack...  Split.TEST  \n",
       "3            1  /home/djameln/datasets/hazelnut_toy/mask/crack...  Split.TEST  \n",
       "4            1  /home/djameln/datasets/hazelnut_toy/mask/crack...  Split.TEST  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Folder Segmentation Test Set\n",
    "folder_dataset_segmentation_test = FolderDataset(\n",
    "    name=\"hazelnut_toy\",\n",
    "    normal_dir=dataset_root / \"good\",\n",
    "    abnormal_dir=dataset_root / \"crack\",\n",
    "    split=\"test\",\n",
    "    transform=transform,\n",
    "    mask_dir=dataset_root / \"mask\" / \"crack\",\n",
    "    task=TaskType.SEGMENTATION,\n",
    ")\n",
    "folder_dataset_segmentation_test.samples.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['image_path', 'label', 'image', 'mask']) torch.Size([3, 256, 256]) torch.Size([256, 256])\n"
     ]
    }
   ],
   "source": [
    "data = folder_dataset_segmentation_test[3]\n",
    "print(data.keys(), data[\"image\"].shape, data[\"mask\"].shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the image and the mask...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OiOwK2L90+xdClB3Py9hiEXk6aFaNhBn1x4XC9+VUsz/7CiODhpJczf5zdb8IAHdfOQqe7bsEZWWI20lg0tWqzGHlbaiiwK6Ao6rZoavO8bk+1wdcE0DXfhshLRjE2gyl0L5JgnyZnQW4I42GECHY6i26nrpO6pNEWeT0/cEGLuy7/ZBI26TKAngKDK8Hvr4cUgwzsPT544nXlMFsdnS9D/qW81u4B0p0E50KXMUAliCaozRQOymgjcHMh1KtFNfWgGJtdiRcn8o+6sLK+gxsp7T9mZ42lSla0uqxlIu6hnDBRtcZptJcs3xwne3wKANJA7A8INPXKXIORGHlBMTQpum2EgbAhg0aSmHJQk6QZlX/xeIKNemzj6Qzi0KvEopcl/ipXauvm7Zg4doflOk3WOUKK6W44LVngAyfNeDP9dHXTLDiqVCWjf6GbJjS1omaEjJsqCSFeWrASz6uTL2wQmMBLMNqJAJAZDpBXjzOHfdYDmNVLrmFeHjWjk+rVdbNMkqFFOwQr2107ccmQqdFQ+qvjuegewdJ0I1j1vBLcUtQBQwVkFavWTI78+w1snRBWyngsx2UOvNgNiqvjW/rAsol1WmUCFbg3MEsjSoizdVTlsKZfXot69Vj1PCWUF3BdVLSFrcTu3a1An/guVRdkXbyAtDDcLBSEO3oS20ynUqXlGoawdrcu/jg3dmoA7HrnevUL3hqvUgroO4d0tXv+lyf6+Ou2dFtWeF8VgIXjozMza1irAKIjCMj3mrFLFGAMmJEUuNZGI4ty2VmUeNWFoh/PZZq7pAkd2VAv8JZs7Fr3F+wgAAEwVAqEhsQkAYsnoH8ZXD7h0snLvekbf1ecC4p0AYQ0AH7chvqH64/d6DNpXe927JsZk5kaZmv/NKe/KW1w/3vE/ulXm+S60qG8j3XMIP1vUstCcAOjqWsRVvCSkakugb9Sm60dmmjciqWBDTaf3bPYM1twJ73AE0KMpN8y5/a6/cpYkGFvzHZ79VE9l6HfQvVnb78liBedYl1cT/X5/rQa/Jq+604GZKVMaUVvh/uCzldoaR1Ll5PPkkpiX1clnFZ6g23AarAm3Opkp8jXJFlfVVn+fmK9R6VDe0mg/IxGfiXtaqnnQbGEwKQpEAjYebaFt5J+V/7gTb98c5wkAYGgRbHzu1YfyR3JpYbYJ1MSQUxSU+5F5oQBeweCCsHwm4rhQT921bXF+/GEZv1zLOyGgPWi7lRbcMprwC8L/Z2np8jeVCh7kZ2minHZwI5Cm7hPdfZg4zj3Y7tm21nfOWC+cMuWL3e2cd1pWv9hhZd0hoA0NnGusmIdyf/c32uj7YmaYKByKolEmPJhilINWupcR4AAjkinKxof2Es/QQKUDPQE8lNt7IiLwAawzKiJ6/SZYfAl3XYdAvUgwyfwnYr8xzpiXLjKWYcSMZNSDHGbO8QjcBcJFE0vt/R+oqA648VY9bPdWzp4MQAq6zNqxesdn4VEiq+NyQqnnvdNu6qLqzvlbrR7MmAFlOeWZON2NR+cBUAnmDuvf2tJRmeG+62M/CekclnNOay1Klrl+1nVlPZWFtGwmRF3FS/eAUEmz3+jQNYDmztwG+8TmM76/WqMFxJz7UJ1VjNPqnrgyuB+1yf6+Ou6YGaBigp4lL1vWLY6t4qQ1AiBIuqvzf75xO+B24JdCyCjZM9CtK0Kp+/xWF2S9Qk1ATZo6Xc2lC2acICrGsrWXQN1ZQvzwPZAsy/MJ+gN1WeLCLMBiEjo2oYpX5DWKLeqqnoDWtXHnXVMPFs49KgVQzaVvWq15I93CrNPTZyamUMtTWmSF9Z9oX/7NZzt+99jGWVJS0Qb73tqq9saMy7LdQ+22WVu+hSZRuiwokrM8TuUDM8f4/27Dv8LiHYd2MlBCimaY+GXkBhH2YZ+RLLUzHTYjnXhNo+HcDn+tBrWg13LO4H1hDahlaqkaupojQuCF2J8q+2mi1Y47ZUMeDTGPcAlp1d1r9NQJU0nweDwB2Sv6ugxsKh+/lf82JzF62sqgPQVWON3dSskLA342lSUmKh7X3rZWS8GynIHOhO562Ywd7nLJJfheg+G9hOlAELGsI6tNyTHvOrpdm3p0e8WjQW8tNeGcB7bdGC+VT/n/mT6VJp3ZD6DUHakq0hXMdY2Jw0aECnYBkopMuvq1/tb3U+L5/3lKbgCSS8kJ+/eNf2wV5HF4jSGnr+VN7SVYUqsqzW/fGXt/65PteHWRMIRZbjxlUHSClNkkTLIiw0oHoy17PXP3T8TC5yfX5HP3HN2ux/sk24P0Ju/QTYDNB6vTtsL0txha5AkoAIjiTh87LVQJcqJQERl63NcY850lCSe3TDVyNRESKCqpLHlWqY+0MIMrjq44gs8AKynpRcBmrDhsyK1B+hha01Dcmu8wwtQOzJqDVwj9ZfWyckx4ThGhdTmN710YWVZ62fol0FmNzVTPkaxEvlJ5CD3YugakQwZfpjV78XFtaD1R/QoHwH46uEnseVZP8+CVi7vX7Y74R3f22Xb8i6/+XV2+X1faK4LH5iViRHBxOf63N9zJV9nj13F2Sx+Mt8nH6iZrjvT2PhG+uZ7F+fNh2hiBy87gscrwBZMl0FASYo0CDMKumtb9k5P+/S9ivyrUgdagFjW0j3Nks9w+X8bH7HquAyyxUFpwAqhRlJurpeE0cqA52dvUAYMeqECArIoVBx/5fBVSQiVQdtVmN5E1RZZ5Ts+Sd9dFz/tZ3MoT3rkND7o82FrHNUF2T3I93A3MdVJ8TUwqeX2bUuBudpFBM5T3xO5fmw7hBau0Np77rYvuXajxXpkzQb2xl4gmgi4rcycE+vtL7IupMN122THqCOsLq4s04kfK7P9YHXBELwrq8Cm42RlMiHmrqzYq4VdWKj5a1m/fWuK0OvEL420v9W20++zGU/LInhV+fXEiTI9ZxeKC1ogQJkwrwZWb+LJYEG06/5VmwELMwKnhrpPqBQwAiNDcJeQWis1qo0mpYZyOYm1xlAE/w3fCZLoTReDmIhObzQmqRsVldBDUiuKZhCUVQDSIWNBZvsYFqF41sGUtZwU0ReAThX+lVJWms+J7GyZZbLrSaRPwfdqHvCzaq9991pf3fz7Bexbzys8s/qBF5K0e8mAVRYn0WLEDnKqm8ZA1r/ujgCrPSxKzDvd+Bzfa6Ptqwsi21PA68H1cwI4zUI6ykGf9fJtSy7VDYL0F4IvWJ85Jxblbja8+KzqwCw4SSbWZEIjGZ4RAmRxhrOfv0LFnHm0vLsILpD5t6sOgPaAuvmuGRa8my52q40vXDH8a/phn2uFNHmv5rJuOL6bKWuK4B8p6Sx9i5c4VK0wgE6/1CO7LQSPMvtm234RqJn67d1jVAFhgzAa3+vLKGlsLc4ertO6gsY7f+f/8MlOVGHvF9lbWudovXOdflWkPHu49gdCQDk4VseOY2qloPlD64hdCsDMbzf5uf6XB9qTRuUYLQWU6+wfIHLVQ5twU5yC/nbHC89/ba5T3r92Sm2hWwL+H5++C0neKFNVcPNXeTcDBFyCvFopTB0oMeO8tZbc4qZt6dptMGSbJOGJUqyGNk/UDB2jpcJQkYETe41pDCrIFkUXTZxxdqFkOchFOxdzNpnK1YldIDMmQzNoqmUpnEfScXfT5juQtoZ4RXJk8ng6guRRYWE8kVayV9LIHqe8ALQr2QLuM7Sbp1LMbDDfPLJ8/UHt6RhHcWG/Kwjz+3vAURfwfp18QXW+99F6+8ovJXP1ARQAFa6Q+lxLx/fDYOxpJw+1+f6uGvmmF+1pNpKw/vRaEgVFwVlR2byTWrYfovQK35OeytdI2IaHcIyH/npBbebdTfvFvKzpn1l6sC1nxWWWwHT6WVWFAyJCL/adLkMXO/42tVGpfNwDU34HBlvSw7VaLOG3xMr7wJv0UzSdw4hKKjmaBpooa6OJs1JacFjDaq84mW1PeszmAF2ui6lq0u7XxBNgSTW8jioGD+JpN3GBYAtlcPLIqdD7VNqC7yqDyZ5Jl1H60OoS0F56FtehZJ7emJVPWGG7/7dfcBK2pZx31/Z37z7gxUWdIqWNf/rPU9xPvOkfQJAn+tzYeZj3XFsGbKM7wCsEKkfV6D7m3CF5wurWY9o1iGLEpPAyx4ApiSnGE3s4TJaDciwKRtrBwwtGpoxvhnA1KUwxDIiWt6FJLrxaoXpKEApAQG2FavEAMlmR2UAgtkqwFbZtaPNZynh9ojMZoM+ZwYs+bkLgcl/Y9VbuZT12uGt7ZIpqJeOhAuHeR6Q0vsAZO3iyhzUh1mXVC0CcX2kgfJ1oa3ntPTurraFzpOwswKAvTAg0ZpL1qdx5WRbsni9srKB7U67Nr8Cjj1psOd0av2VnR1FTRXNf3ufc7Y1BGNzRvG5PtdHXktfgSSs1bU20bQCRhKmSOO1Syb0+5/mZGVZcvmDd038WZHbDH1EOKQlqNBb0/X++nPLKkhEgePVtHDZTK7+flYZs8F3FeZLruqFCtfo4kTasN0AVcCb1B2huCrWQpO4jH1brqvrtc9eFR4HhyX5JKvBEEIUjTbM8hAL5AkZilaUnBrjKtPTaNykoaUuEZd7zphdi+HyZCi7TCqhagq1d1d619gOUOTa3EqBbcreC2X/cyUgqQaxjPV1Pu2q/aw92UKBfywEJzea2ZZWrhtsvz+vbaqLMM9ftKKT5kfp/UY+1+f6mGsmCrGrBWR46eH7wEVe4ioBe8rZ1dh3tDwy36fX2qR+svqasW1mG2mIM2lHKNRM/MrV61tcWc6tDtuKf5t/3vt5VSCUynFCk2o6UG4aZokdF2ZMQyBr19nairaNUBaWI2viz1ZeUsMdzRQK+XIRmz1itilkibRkTHvEZKUVbMubhtMDRfhBwtjRfpGdopEUnAazyqvWZeHWX/18fnRdVVSd4MoA1jVVo2GFbrXh3Ka49NlcACDyTrmEQ/CkI7TcQwpM4bLRhTouIhk2nEcN0+WN+pt4IrO66FO3EbbqGzPIEFnjiTur20r2n+tzfcg1r9Bpy76Bdy+iDWuy7oDisAQaxdijqvVUN+cdu8koUKhbllA+Byk8V02qyFmGxhxgoogkO7YrYroOFRYUNbBlVac3PYmN4ZqapSvIrXYmli9ILuUzfr0MYtjoT/VIkyvUbWvFaijbEOrFOSTcEzpLxN8ERe5Ba7p15qBYTcVlgy8DjQuC0wq6fzvbKmP1ZKbu6VF+QcqlAj2S0VhzCdDCaou/szqcE7ijiCqedvdyNlUZWZMewBzKuM7NQv8kOWDZDGEGs8Gt3tt3xxNtjFWxX27vuiG3D17nZ6nOVXrGvjKQOk9VV2b2s/q5PtcHXBNFJol9OmuFgBtKuw8aRAWGiXevv9J4acusR7ct8oURK2IfKJ/xXDBs0TIykaegoabo5JNLlvDOstHDqtgQcY5xER+7ApHGuZg9QkoWNP2yHUJHgs2j6VlRae/LroG2jQ9bLi1dkwWqLkBmTYWNN2USogre0zLn3rCj6bREktaFyEN4MuptvGBm3btg9a3pJuvyYeVE1OUZyk0IQGcSaL5Xg2P1jXUgfb7smgnaOyIp3xDqdrSE3ZkDgRCCoByRc9ZG4Ss633vetho1r+vbxrlPQqdvrYSxn5z97upcNl9UoXcJWHn2bLPcFK/c8XN9ro+5Zj/YBcgAyypda0/hgUYD6kVT8u6rWPiXP5iIyAXapDWHgPpT6gokiC0ho3zXmWJEUnQDJ/x8pIoZkcILlV4sZ7OcVkErlEoFutTc0iIkW3z5gexmSuHSTAskLWsUWhpqdVyrtS1PSWcjQZgQEU5w2MTmNfP9fVqqRsLycKkDEWbpMrpC3KfxguPAypYyL8mx9UW9f5JYkOReoH/F9h0vVxuw1AIPCXJFW/6shMdqXkuXQ1q7xrKfzR0o7XDjMM5hR6lAyDNrSFZqp5WwZ3Hp/DcistxhuArpi3HE9qNbkro++nS/bSMEJIU8fUCQtFGCH0YDkfWPv+pp+Vyf6wdbs0t/2BHtzYY+kfCW7cg6KsFQSTVA76Opy+r17Fx0XbfJP0k+NYiiA+z0nSuHB1azmFJXeliNpVnYUtu7quvW025GDjARigLzF/CdIExuYIOMczYmcxpahdgVvpLXMNuKYVGAQ+L+kdZH5iWYGgmz9HlLIx7pXpN8i/zrGBahNrj5BRuvBiLofpVkqlrQFrXOKjFsaDXHrvOcTR7E1ScsLbmN2mC7E6CkAA3whcj3PuTZys5kqEEksIfwDOpGfWG8ECadoQdwJ8/AGarJ8vmdqXF0ATLP9Ykrb2n0bN1LKyHoQ7k2UnnJcwmh+ubWTUlZlwpohk8H8Lk+9poNv2clTRJcTmZ4fAVouaLEL4nFBWrxgCUFqid8NuPKhHrV1p1tW5nQSFZh07Ch5EijUYi0SmnnEsfogLGt6rv9bFMC0gBPxbmcg5gx8qp470YH6NKD5OeZ2YEQwwbQCNiFCbVyf/GdruQjvSMx0Ii7Gg0jqNW9JCDPSeUgjGpi4uY1gO6GwDOm3jMX6d5RfyExWKmPisgPMvuhyrim/Sv/E9EgVR7dhu91c1wXTLN6QiNC3oMcMDjJgxrEYfjJ9AfGV/CAy/AQfhb+HPwZOnHBOwv1qrW8XTGSVx2/qj7WrH31qXtfKM77YQcq18XNmEZJCa0zanhqdvlcn+uDrgUB7ehNNf3gyapCiLIdSsvL1qUpI5Yl1AzeC3tJSFoCjGVGWaXHRCAAIbxz/lagbOZ8OYcVC1fkugNNi8KUKf92LKlCPKxG3XAdW3iQI01HA0fVr5BkpcL9m5lpG32wKEwFodd3SW4t6daWm7zqFL1LxTjqBof0Yd15kOZdq6GVJNMfRWMhaYAtsX3r8Vt9qtb3XtyWjonz69SXtuu7eRVygHDZQosQVcesK2dLk23dvedmSLwLmPAb+IX4gvgj8SfgJ+AgBvQA/7Po0PcsJjOH8CDSypPrdNWx6wIX0VMDuNRVV4yfBV101/ZiB+W/V/K01417s+pLUKqnn+tzfeD1BFJf5r5MzRXYpm58/rFsZXNDdBnojDwTSCpWv3FF0Lp8DdLIjTSMo2qqaze2umy/AiGrAB7Xdt5V8dICrt8SUclJW2p+JFSk9d4VbZZ9B0UuU8OqTlcZo9EhVnycUWX5oSebcn3vqluunqyKxYXszEJDUlgWT6UKUcdy+Zu2biAxxqJFqrt587Rw4SLrxJbb7iIrOvLfzt46ePQH0fBdbiZ6twikeuowfjX7A/ET9XfQn6gvwJAIPMRfxIN2kI9A3RI79tKZHMofoEhel46FGuTqW3RF99ju23Vaftuk8j6RzehAsb/zc32uj7mmlCqbi1qeRueSFM7Ue49my8RciLhWUG4Yl6nLOmScxWXnAknyU9wDVOMFjsQ23RcNAW/zGgtpKU8DLlrkCifXVrt3rOoWUnHV155ERKNLVQRIM6Jm9KQu23XUUMFTqZPP5OSsBuayaMZx+c5KaC5QpUqmYKhmRrKp/fWNWt9WkfKVnVW+coFCbUzLaGprlGUhbyhgqhyK0BVtVboV1hqeBayjnFW7nuLTgBRaECm/A4YYiIP8Bv0EfAvdmDE6XJYJU8KMBrgkqidzJXL4jnTAShL7BsibIVDMY6yOwVRvXXfp+nmdqO1+WLdZCeC9m/n5uT7Xx1wzw3lgxbMN80gduQe2B0l6dgaJ/HhhNcWbMcwxFjwtiWiVN9W4dgkhX5mDkDF4bj6HCAM5ebChJC0UuOLui2BeO7PlMRIgB3P6PLo3jbuZWFaDgEJmbMhe/TJAuD8y+h1joIe0KDu6VmV5R7RDGHvBAx3GbplNZU5YTvdaW1Sb6YYBa0LDMmibw/Y1OVkFiIMcpYxN06WTkS4BzRcqGDyAWX+KBbGXhxNLp6POWzSLq0Cn8u1B2qAmwhQUETphD+EuPIQ0uCZE+qnE2tqxXbE5wfYYXagG+2gTH+M6M/ISQb3O8/PSxQqtM5BFl6QLpw/8bz0gn+tz/chrZgBbmfgyqWVgKxwmFVcUvMXeBDLmrYjfEvlhIJIyH5HNu2iDKaR+fKmypJmMzZKikv6LkYI92L3y+izqql8sOVI08J0xY1VYLw3nC0tZm8JmPvYcpUUx2HNd0rD26FtFB+t4b8HZ7ouFt+ymZlnepD9FdkVUfpO9YVK3YdOMCnK8S4m2M6KVamRonLF8EodqaHCdgVUfZo9/yGtdE3wrsEYLlLZcUvrdZpHmbdD+AwSdll7BpTPigaxb2BvsO/gmvkmPaOAuSlH1iarUblJx8cAqDsgkCau4hKvWgmaJvrua+/XNfy/iaY8CfuezP9fn+phrujtb4mA9MlUUhTJZXnhIfqZaQ3Ps64XjRLVHGSR4zc/K92uB82QPYFmhWUsAdB8YISvc+dnSvdv1RtgLkc796Igvca1larMecNnK1bVgJX75JC+8TG3+lhZVqzpdwkA7u7/MZUQoZDZSIbQwpMbT18Zzx5ZPIlqPuh3vtufXV6RTXOdjc4R7rT7Pcp69ymfw3jiKVGvMSaANq3wvx0n2xZJkwyoxWySlko4AMCQQQdzBN49fBl8kCkZzju+yP4O/mp0Z+PceQm3TrRv3ntz7hhGi0s1YozHRBr+c+/PHFRVtPOeC773CfuHwuT7XB16z0fOM0VOuBs0zZJTSC/DMt8HKBjIYXErI+XRdtu6padaMEZ4QduLm2AaFaxEW0zz0UIG28iuM7VC5IlM8G1luEy7L92zNQYBgLVGWX4fWxEfz0+swEz3PSTLL3lzmRI39sAE0ILJ8Yu1B+kRtGQYAycmUtk7TpkI/ItoEXlbblknb5FEzjE35Z3AVitGHSS2z3Ta1HcaTydPWyN2M226cqI9H16hptFCko8yCCknIxe89MmiKb4DBHhjfwT/T7sADVMiXB7cs6izJh82loTkEaHkJMHMCkZcK0G+P5UqPsGZYNmLJ6wxsN9tzXvm5PteHXBOgWcnBJ0dFEQkNr9z53WPzLrDKHyzpiVfsvMLVYJmw6Jgdba3Wg5kASzRG8R7Tb0OGZU6bolg7ieKrBHXBysVieubPLDk36tqJ3O22/sjCY0CEFeigJuxXNoRU8QSvimodSDZSdQ0zul8N5UGobI4F0u82A9+WYcd1mKwUC+9O9ch5au1kEpq3Qpy21CTBd3JU0/RGobl6xOLyEwpxoJv9wAwEQiQN1irTC0ari2UM8JUAdIIHmMSrB+0OnoJXkYmC3l/fzXmvWRG1+So1pExfFSfQANj18e032EgxiuwvuciguxtYt93n+lyfa7bVQFvUjP0KCKj6IQbRnULPodOyKfXRZ7imbR7w/MzXH7ct4DePJbmR95+g+YXsg9XCWh9ZgjaFK0XwCTrIhqBdd3591wY0tfVhe6YEIy6xSm0fryxIDU6z9JK7W60PZDmIMr2Sg+UWO+NJS7jcQ49IXB25mzlbbyghjbTdT445DZ7lUM/QQlHSo1e+JgShwZEnpyeEWiiW1ynxvr6cnWlt14UBPqAA38xmCp1maVh6gF6ObbX3tgg3+zL2t+RJU/mYpj/lRORW868r+Hy3JMfJloPfEcstc71u1x0I+lyf68Ou7ATGwisWHVMRrKGyy+SWGVpPUWo6qjs3l5gbm+syhoVf8AmeaNpXRLa7k4SeyMtS4wLyO5dv/AVoMiaQke8qVmeHcOIm6vgzzfBSk15+JaItSM0PiNLhR0XBITXXs4LyKFVhRUTXSLIM0Ai+mboXN21mHmZsoFN7UyxqY40x6eONZnb2KW0TulxmkTurq2t5yfQiS1duZVFbLExVTdtIyuvCAQqFh3OUt0iRi2y6YqkxswSWC7JJyOgMncYHQGIkaCS5x9mZ4fvEbr/0+e198isUyWu+8c4qY1xRyOYGrh9XSpdHmpyf3WFIT/vzuT7Xh13z3ewXKbIbn08Ppy7BTFJCwsGsEE4LmF51wvqYUhRTIQ9hjGFm7r7+2rHbDsu2RVvxYRrdnThItDRNV3Q7LEe3BbDMOkExKaepxa9C+XNaAABUg1LaOxTKoW77QgMiNTznSj/U3J7krS+EJvdkhx0KDnr2oNdJqF9k1/gqKKdoyoGl6JClD6dmbjJr93UCRV2OwbrZKp1G1ktsO9WprQ0VAyh9H2qCG1TEzXX1SSCyvt+RgMzKzWRorvJY6QweCgWqxB66+ng7qRLYVSNQoaQG5JkDFhNYiuAYpFVecDmPPRO9eD7v/n13N6JDkKey0Of6XB91zfVTp/NqXYeVNWdAPYhoPrsaZOcyW1Wpu4iYTbFPsDmh8crny2009FS2LPv4d1/ApePcg4tzjzouX59eaQc3pZ82AQCaUSp5zRgoxD9FP1n9QTW1JuPqQt6NdjXLYoeg0+zmjBcStux7Zx37z7k/S6vSnn3DMvq7gaZ7Wv8qCquhpzb4BJYaglAokJVLqn/3UFdIRevSRi2gqnRPO8fIXKaZ8pkEZWEY63oRZjxCWc/vljezAs8K8ZMYuLhTuQOtMKoSkrAtb0OlfQvDuSx49eJVFogW/mPfS1c2ubeVrTvw6Q6v82yLZfC5PteHXTUPYJlLsyx7IjzM0gpUqJtQcqlw2krLE5a9tphJOtoIZQRXJgRADqpC0m/Ub2dnAxXnsfD0Fdlfb1to74oB29kU8IwN0dJS1QFV3f+dLgC4qP2LiakkfC4HFgFWcVKlh5xcnTxMWoQMVkA8qlTS0he2YI2MZ9vQl37ydiESiXLjUOm/Lb1oC8Hsmn8SS3tDXV5N2J0DuMw2oFKpi0bhMt3YKjSF3qxGDCwm6lY1zSyBtkYjLJ8aFiiTvbKN5XT3GPwSyZAiZMkLQDBx/Y1dtq7+qsWsPG9dssvl1y3Vtwee0tZLMXC90kEH0NMd8Lk+1wdeMy5pHZTyZc4W6cepI7CsN5a6MvJRL/GZC9PIoVEJ0Cw6YaUF1e5U5qCNwtO3oGK5thdb6+x6yNvY7Ywj7EPMV4LS70H/usQk6m0RQY5OO9hweuURy8pcEFOZj4zIWR4D5kjSUwhq6aH6BpSLikbKYnmfHb7IiTaS0C1f6z11CAJ6bk8egtnFN12gWSZHEZu+XHuaqqnkxrqTK8JVXrJO2vKmmSStKxsR7XUDLPXvIhAopw4EUI3BZcobKCMNnStUBpNHt6WM7Dp2NFBDIJaAYFOAtvOG9cFtt98b9P0N26tdK+b793+uz/Wh1ixggALgkfqOBpVi/sJnOtC+gue2C2WtOogr9mcF0eAYQxU6L7vAjm3ZAbVWEzI7dQjPubK2ntxNRF7ZodqmcWUd6znP6NvGWFhTJTrbR7QOkBBg5DBKdOzHKOQwyAhHjQwzVUdYDq40hVK4WFqyE7HsZm4msuTa8+jXsaQH6tNr4UFe88gAnO5cU5JZMXt+RzM4MedIA7qi4vM8lw8b41jKmumNenpzwnKb6VR0l1cWw8t0lthy12zyU0lFRfWR7Za0f+ZqEyx9B3ICGsj+52wSt87q2Fe5GkeizyGwaMqdpTwzx7aE7wIeUb7wEgdde6cCnWr05uf6XB92zYxxOzwsjD7Dw43MnpamIfOKrH0F15LQk2NpKW1PXNKXNfpWYOvbr2+pomKF1hfIs3rRLqR/gdoAli1LG7oQko3why0GT+hZHezXLpOr81aWU8CwLM61n4WAMWdvWeuYkdnVLEl9rlaE3cEvOrruxClTI/npfWgtfZ9gDModKmRmsPKji4DEGr+zDGD50aqY9tVZ0qRmo8uq1UhFI6NCcLKkLVjFczVGjj7tK124zts6/L5PhhTuObc3j7Bccl5xCV0oQv2RecOMlYvgKelkNC0qv3N98eZclke8sqUnQ/+8x1secL3LbPz2/Z/rc32cNVG2EEAj4IjSkOm14wBAqeavoLUQkn482YBtvi9DsaTKPyXhANXzfrFx4dPTVLvWMqaNbGwxfm2HV3l4DwzVRdfFWc24ua0oPRzlsZCvtdzNkjsDwChl0rSIibpk5DsEI2aSptwjIX33WHuhHmVegSuo6CKK6J5c1XRrG0zBmnEMYI1Q6T6M5F/lOVwmGMvuq9CNeh0s3KtrFvuA+K4IoC5S9hWv805WAwG2RgQ1KJfJmspz5Pm6xljqavTtq7ni9/YrSZdaN1jtExHhtb/9mnYMqGcJr5tznbd1Nvbwf4yx3taect0q2E7G5/pcH3HNep46PCxyp7gQ83pjGShXI0JsPs3ChdZjmY9ZM14udeJlAZaNyS2zBXnqq3oQfBo3gEno3OV6+p3L4lyYiS6ZBJa1SkxDStak2YhKHRbBp2iM6JJD2rTMRoq2qnWAAcg4ETN0pgCqJCYhqOxqWeMIgY4aBZk40kpokEJMGwJTxZLMbJIvy1V1IUgENdrbbSavqyOZRYCA0ZKtf/U99LmqH4yWM9lQSUN13lX7t6Km33DDcvpsK9k8LRsUEe0bSu6/pZvV+1ioIDZ7nZ+1zpDqLmPfBBtOuLK/3J1ymJu0Q94enUFefQB4xoXEnsOjdYN9rs/1cdessVBtU5alD6hLqPmoLMvbryTD7zIuv6Vb8MLl2UPnO7Jue3DByi32IlRddI/30QaxLFRvuf7UhmbDEHo6ysoG2Jx3j9SeS7e0LL4gj54+30QgU6BNeXREna0SkkMtXwwowlnDhOHe6Fj2TMnTIC61nzxY95CcmcV40IaRQhWE2VALVvBOGEdkKmAwG1miJsv3WI5jAAafTt1+aS6v2SeQ7bzzBqjaQuNFuj7F51Odv3mzWq8Qn0atifG4hOf2C4r2W3UhmwawufkrnmAXhyUJkYML1s7sms/rYPfuX1wHiA42rtTnc32uD7smevbhkw2NtJUp9EZcoxaBFhZGxbrY7EmB2izdN7bzYGPyVxwnLeBpNXNVMNg9/ZXF91evNN86Sly0+qfHeFn8VaLoDxZAj9I5EADjkCLKO9V7BDMOuQuKiHAYzT0i3OwIeeiEoumRAYoWQMBDUAkSFVE/MY2ejYBUYvAckKDQmX+hJDFyjKUgePRweQqwgtIBY9jM6BjDMMYwMlBeJA+BsEgRN7sqKImHrFOqi3mVPR9s+mmfpaoMla0HsNqOyWFmXYBBv/mC6XO8Wp15PNnZy9PoeqW+F8VY7duhLyX7/loxCuvOswUJsRKRdau8W+vFvlvM7AkQ+1x/7ZpzuvtfPNvv1p6Z/Svs2Of6p6+5IqZ+MAoKYYfSxhKWxBOMU7jsuqjZRNr5QWXkWw2vH/EaF1ywNbv7aP3Q+wAU98O2LKSl3BSl0vmbp/3517znLqOQDJdMeSKCNhJjf/48BZOGP9zPx93f4hRFDwiHJChcCtyNICZpNgs+ysnAYg7/XXizSarOLUVA7oEQQAXAGQGPU2X9oyJcYCUHAFglFYwhD89GCFmPu6mDlYw2aAaDCVFapzTwSe96udU+3boupSBVPZxWAnDpspPjud0qaJef326F7HWJuDz9ese7Iu2Ww/VUot7y5a2xLPuWo0D7JfsL173yxT05eE4LLujsc/21K8sqWaYaY/xXfMCcU00hwT/imD/X77uqCPyElkayeDhs5BO6JqejhRYktQJwBWU5JXzBCD1yRPt1J4GcbQKRkVFk/kUdX1JPnZwJTD8brH0OzLu2z7UDWhHHIrEUitKtYYbUUYMrZCkYodSseNzvcT50nm+vP//86z+cbydinnHY/MMY4+X4KhiqP2tmYwOpMTGnzGDGMEHOmt2YtEunxekPR1AjJ2tlNd0D7kC3XqOmoDDlz2gkIyWskf6suD2QRX4d2reFYmSvr+WZD4JRY+ef/HfG4CQb9SrMPXJcYmuF0szDJV+2fs0mU3cQNFi/HAMXsRVP3gLqAlJ+BHoyCrpIXCubfGryWpt6t80N5KnXFv6z40IR0UJTF8r3335EPlevbFhx9+M40gHc73fr6RBruXt2kr8r2pnZu8jjc/3ua6KuU5I4hyIFyDIaTYHGAnPzbWbFMNyz57at10sNzgAlQIYGehchZCfwYVVf+x1pDt5hO9bVSOGCmHsHykXlBhdehDMauiJt9YKZyQqrkDJLkELnw9/ezrfvev3lfPvzr/fXX99ev/sdpi+yl+Prt3H8kV/+NOYL5xTwONOlYAwqpHAjjmNghPCgHPKIEwjyAZ6he5YOEPQ4O3mCe2xHQcAkB5WWl8wmBauEgByDJCPOtHQppDEGbdSZfzwe6QNGuoo0gwFsfbZlDWEre7Nmm0pegwoieCFvTxd3P897oL1C9UUj/q0d1yJHZWuyYk0lqjwxaqDbruW5Noi2+7ljeYNl+oIO89+FNfU6q09bmwjV5/onrrwWb29vc85sNPktL2NfmRxsHJBMYcdSA/tcv/ua/bRsFppp9LuvFbZkCRrK7xCxTEBb3q4GXnBwGq/Ou4HLnaxA7MoOqx5YvwBWQ8gvE2PSuSoQeIoZq2a7gCy24mYmHKPInkh6TECwNNkh1+kuBYK/fv/+/Zf728/+8//x+vpfXv274z4n/hD4ytufbuN/GfPv4V85v0GHR97fPWjQBQQmPAbhgTPiPqDQCQTtTt2lN8Qj4h7+8Bjhj6TpqFuTLsJlWBJMI04ajXKzDMlBRGCMUQh59+WmL0CBRc6Qje6kbdOKBaNnHTUB9f43BT7TxeSVjZpxb50ZbOWf7SK2S7hMdnYsRE6B3+i5EWE2Ln/AhRchDXVXibjtry3a5/pS1p2mzjLzIArv6sN8cgPDhoBWtsY6is/1bi2zvks35nL3fGXOkhH7rRMdPejtSrYAd1+eYPG11jX9XL/XmhGLiQgUBo4m2lz0RJIRKYBsKIZHPcPuj8oMaNtF3YVu1pOWuEEhBs3hy7tknxnYRmr7eBcDrGQsNzh7vwUzzVQEs84AEDasi6NiEXuawSKP83E+zrjf49ef3375859/+S+/nD+H/9ns+zFev43zj4y/++Mf/xPwh0P/0cb/oPGF4wvnbbo8HJaTFEOCIv+jIcAz9EiJaOGUTuIOvUrfoTc/f5Ue7vfzfMyBCITOkEd4wj55sJGqy/Q5jRbJp7fBYPgZY9iYHJb16ABMChuZRcFGxsSKiEWkT3rVysys/1ADA7AIMuW/V6JmNlR9GVeysq6Oma0xBu1tKrPbo35kQULlMhM6XNu5nPc7nQ/PCUVXCUFbOWHdDOoOslapegoUsGpRlbBi1xv/XPta2iSLZJGeYA/hl5X/p28zf4htIPMYo5rMP6Gh32lNbM9JdvJU0RFLXpipc9CPdbX+y3PcSjThh2mdU2aZfAoPVXOgOhK0LPxaP8lk4hS0QeP8C9FBogQZ/i98qTVBmRW/iqOFhUtmaZaXRWqZyuRahh5vj7e3t++/+uvP/vrz+fN/fv35f/95Po5x/3r+evxh/sefXv7nP/zx/263P8a3r/OnP8XXb/Fy08tNY44g/QydZ9yFCMUgBweMDqcEhiAwyABOYyDuoe/kq9mv7t/9/EXxesb9cZ7gjPAlXcCukaYHjpBRoMwwwswSogkhwoYRLF1QuQfJOUf2BMgwRo4bY81prtBZY1iLbKKlsIFNRYgdoqtdt1nJMmdUvrOwLrjPUgJvFQCeIvG8bumaAh32X6MLJPXoOJTlznk1V19YY0qdBLzfAeDJJXTeYLtU7drUf4+H6Edb53nuOZak8zzTASzD/dduc9n99aiu9D1rxZ9u4HdZE02mRIf9+YCQ1b77rm62uC0dsl+PYgnx99OY9X+zYTbca4pIBuLReBAad0J3ka5HV10qXE/sakN7zv2xwkxchqZ37ol+UKO9JMaZcP/b91+/v35/+/5nf/sv8fZ/xdv/6fYPBx7HYX+6jf/pTz/9r3/8u//l+Ok/2rc/xreX+HZ7fD1wm3GzMCIMMeF3ukU45DKcpAdp01q8AQgbmY9IfACvwOs4HhE/j/Ez9Iv7z+EM3CNC4VnlvhwuDMgigdzjODCnxhxjIDWbHTGGhuGBGGF9rWQRY+aZzA0aaeGF8nVmpxJxs2Lrt+1uVB3NtxK6/5fLMdRSdWPTqulsKxJcj3Rdqr450san08pIIksUaaUTyEml1Yo/njsB9xSkKExN7d1N/LJiXULX9tlOiz7Xb1Y/zkzQPwk/+afzPCPidrv9tRtc4WDCRyRXIQGfzvh3WhOlCZwpdufsav0uqQTQOFEmmwhBRUBEWfAsF7eJB/CEACq1etL6AwVTpyjMpeNQXVRU9SFXVFjmZwvoln1sI8WWGCrHk/buqiwn50WSTmCcJ86731/ffvnz29uv5y//8Pb6Xx6//n+/8xceb8fX+9/j/Pqnv/9fv/zx//byh/80//gfjj/9B3z5iV9f4sX45RAVlsKVhDsMHJMKVNlWQYwxfDlGY5iyDUzx4LgZf8J52vw29RP1Z8SL4zjPf5Ai/DzjjYTkkIQwgxCEmxlkfkoKDwzjnPShrHMcB+dk+MniBcl5enH/QwGzYZZE1cbuFJ1VUIg1VBJdjoGVtCcTtdmw9XWJ8+YhGRKlFIytam1odZFcuJY6wrDt/uhZCpIQyj+hqhpPSOBvbi0AqJQ0rhvjigMu3AdLPsrWdp8UuT/0Wg41m0WQufxfqtYu9P+v3X7+sHuOhIDc/eXlZSFOe6n/c/1Lrwmg2ZFYPr9kLmFEjn0ydJEvjYCopoXYM5ZqLbtfl3Dpr5GVTVzhYceDkvIbmJPYC+h/yu7fpe27J2htovxrgwk1XJdpfBQpPxbu9/ubn296/fV8+wWPX+bbf4Z+xpfXm73Nw396mf/T8dN/uP3xP93+w/98/N3/OP74p8e3n+L2opcv94N+jICSAyXvzqkhIqBZf2HOYgfMwJQODSBgAsxwk1E4iRfTi/Qy4yV4BKbHEW4Kc9whMgWUajQjwpUONFKq06gwGkiZBXBKIqsoHW4cMSLcZRxmI+2fu6/LQRMJG+Y6DQMCq/NXGZUbR7ZMFLrEZ1vM6yokZ3Qg5V1zdJgk1Zi0ssXZ/VAt5og9om9V8S7h9uIy6M+O/4J6Vqz6lBPs98rmMLZ7sjPazwXguV6Sdj9N89+A9vxVK6sLj8cj92HXbvpc/wprZghnOXIxs+KQoeWLt4Av4/eFxHZPFgrSLcRCbbizLSjYbUSZ8C9sB/VYogNPLjme/UnuBzsalMBuBXbQsMzHVesjmwwvBWSCnaff7+f97Txf9f1n98cNp321MYbb13G7ffsy/kfh7/Hlfzj+w3/SH/9O337yP/zx/PLFX24+j9OI0YcNBM6QauIkLq4bCTESC08cPREIiGTaWQs4NHHciJehl8Nv1Mupr8av98f/L0ThnjYaLvEwo3AC4RGQOAzdkm3GGPK4R4x1XWi83Wwe2R8go8ycVmacFCiahjE0aJY8VEhzkNnRJii0c7zzhxUl5ODQzM6q6m6JKEVa8dy/xcnRBdRUbtaGuy5Xt2j0JSakK0Jfjh8byMMN1dnOP/c9ZheHxhg7e43pYT8XgOZxzTkvGnGze/+lV/qYrDEcx5F8oc96wL/OmgJHqnd1AY4VNKfSl8qClwZkBXcA6pFU1VqBonY0DpOdPmUWPRQRhhLLV/HEuYq6VtjE++GuWTJi0d9zSiJUCvV7fgBAEW7Jfck+tLY4iir5+onzzscrH9/N7/NxHxbHbX59+TqPl28H/3jMP93xk3/9u/NPf69v3/D1G16+xNevut1iDJsDkkJwFySbGgCUwxHzHCCbZ21AKoFQSDSJHMl2NAkGEgZO8gCOETfyi/Gb4ctX5/fH/+n6lbwbnR1oz/liFoAHHgIkcwdpGgZ3MOQFndsYncRzjEoLzGSGkDP1cOgcGOSwCRugGQeJ0zUHIYQHpXQFjCzdWheF80ap2J9mEAxwd7Q8HJozpKxAdz+2mUXzjlKCIiJ329I/1ac3Euju8vFUT643JHrwLgPQBhjWduLyGdqETz5XrtXnledn78D4l1sL9snvSsfzaf3/1da0jtVyLfJJgtdKVch8/o1SsAhBbaUh5aiW+rSepixRjc40YX+L17Jolw96RAxe9GGkNbme+XQAaw+5IKY99HtqHLsEi+Aud93v4Q/c3+x8e/GHnQ+Gj3l8G7zNr18Nfxjj78O+jfkNX/9w//o1vnzByxfMF80svBqLClkydXZMZPVyNcNnvoIOYHvqGRP/kVYBPM8HDRiCDtONNo0HML9y0l5cv4x5Gh+hEwCGjFCcoKcEaUSEnyAijDSOOOVmAGMobIzH3d0xRnWNZSqQkBEgmOAxzCYDZrBhzMoy3LOuW5g5DWamcBhWeb1yPsPC670VfgiFK/JyKEcPV1BQpSYsATt0dQdFEsOl1qmthwjPBQBtWH+fde43w7of0pqtO2H/yHUffqS1c/zzlSXVkD+c5znn/Fc7M7tm39qN9fp6MVvP3n123QP/WHPZZybx31yXFMSKiVbQtEtr9UgrYFUIwchicYXao6ZBovXhMnQtDigXESOT8OxqFZ6+9B34s/ZSEsCU2HyHEeVfbZhqdmBkwqKClBiu89TjHq+vft7hjxmPl/OcoM2XF7Ov48s3zJ9s/r3j7zC+4fjq8/YYQ19fNA+NWZGpkDi1jEFT5JiuW5yPsGqMZvdM4ZI5WGiGl9hFAKQDMli292oKA6RxHhjGY/CL+6/gG3gHHRZmNTkn4vR4hNzj4XhTnO53G3MAwkMWnIoIBSw1W06dJvIM+Zwcgwn9i7DRTR2kjUHzcNMwDbpVU93QHM3a5ILku6pOVcCfyg9GDFpEFZWEdgh7uJ3azlUBrp7BvhMurP9d7LlH/TtGXG0fT20BXDdzswsIJNJ41aveOZWPs3aO/3747/ppfq9F8vF4JBK143tr3xZelAVkber0ayPruD6t/39zJR8LGYjlzdHRFLB8QIXcaNZ/t9rjAmvILgMIZPaUou271kMrZWhm+/PcnWjXw/nOvndtAEtBoGEfoOfHFuVDXVrGiMDp5+l+v/vrr/G42/2NigN+Cx08Dtih+S3GHx/jjzH/wPkHjJewm4/bOSZvh9swG+AgLZTBaRr7BsgiMKaZqbT8k8JiKTYg5vTLLBjknVq38yBokOhOHnPwKxhiMJsJ5i3Ob9AdfGA4KTMZQdnp5+PxOP0+8Hbqu3CXXhmOCOJmBsiNnqyeUUJx8fA3P90jKwFhOZX9ZEJYJGwEecYYMccQSdi0YSMpPCkWNJB8pOJQ5mCy5NdXT3HCbZAllJdXUxhcMw1SBA817ysndwJSJAlBipVS7I89r+aPy3C/6yx95wl2P4G6y3lNuWDnlR9sveP4s7mY+dff1w00MnzpPC76aaO+ZT32FmU2MvxusfvX/jUP4d/dSjXQWCqbbWXXo7MhNk0Dyakm7Sba8Jf5VoIgI7m94a2/fimyLRfzbOWxQntdbmX1B6EZorUPNBosM9a2FLyCTYwIPe5+P/18xP1Nj/s8H4efI+KgXsb8wnnD+Cr7ytvfY/zhHD/h+BLjkN1kh9s0mzALjgZNhACHrEe4SIKRHAhGuBnIoQBlNBPCkLvcxjERbqPBaJZVlZQJFWRxAF8QIkG3MW+M0yzAIDUKIY9xBnQfdvfz+8BP0jn4esYb5YIPgDjNwiyEGEYQjsfgC+zufvc4yRPIuQs452MOSRpzDKMfMSCGzHBgasDmrP0mwyOxgaUHx3SBNWctj/LiY7HbCRd7QFX/XxF6bloNBIlLlqotVF/9C9Bf+cE7E58v7jpFfbcsi5aF+Opn3vzLx1ps3lQ+Pgvr1yXV9/usJWy1lFyTFJTXKcNTu3RTrrwwYau1nUwg3kcAn+svrVmmE1jnM88b7TrpKxpbpnnrxlqf3V1FP7115bjw/ErHwzMW2yMRIKXzK9/Y1Mfq/aUPBgCyjEdRnUfd05M1ZHrIXW/3x+OO+1s87gy/+eOQDuBlHF/HvGF+wfjJjj9h/BTjm+aXmDeMm3jApmGKFgmEM9DfPo3FlEz0qyaO2ZjG5ilRRGDQSIR7joa0MSSMmgVPmQWr5qkslWJmnRZ30s3kdE9aTfaVkaHzxIgvt69+vrm9nOfDgDPuD38DPOKRzWmDGAOikxJ9MGTnPX4J/xU4XW8er5AbpRM+z+LfDzPBA2YYw4xhRFR7llK/J+LC5esS6ymSrunvRQNjIWDq+J/Exs3f7z1svgO/AWcWiLTcwB6rsuC+J6SorQY3O7B++lgV4OdsuzgX7yR91tt+99NCcuee7vuz14T2H961JuzVgnXn7P5jB47M7LfVhY+zWsl+P9H5jEVWbq/hXNhukQ6vuNT2V4xGLr1PZZzVH0+EZj3nkSO5lxjIQqKUKUhrPzyHgalYoNVQDuCqJRbRHH7G/e5vr35/03k/Ho/hPsIn+MJxw3zByxfMrxx/xPyjxjeNF41bjAkbwASTk5ATQwKwZMgniB9ZIjWrob+oxqW8pWxlIhIkwwgoKxJ51oxMdYhaCE6CRjvGHCLAMCf9QctZklhK2VlXHRAxTbc5hNChuOmMOKUzdMKiFEDhYNgI2Umcxu/Qr+fjfvqfz/P/gt6Ax5xhEWQ8HrJpc44ZMGMMQAQiXVmKKRm7xoNlUNBDY5IjsGq7dR3VCkHrxujJaK0zobxnKgnID60E8S9lir3lLRUohBBoDaULTOjnHxkRosrRuGhGH2DljbZyo3fo/0dY53mmU0nvsggmY4xsQfiwa0re0o9YQboyvgVQ9Hb0k6glJYbrEdW7YktjAIFtUIwuiTf2xq+1QvveQjoAjTE7V8AYC31CBpLVAlb5bGHIEbq/nW9v8faqx938PEIvwk28jfnN5uTtK46vGl81v2F+w/wpMDEmbdCmYAn0FKJRhg+0pOWj8iO2WUY1LfcpxCK4AIAHFeEtQlRaOtUbgWqfSmNljFCc0FH+s2rZiIBxepzSBMMmkvw/ytnkIx1AeDgGgWzvcjNxBMcZeht8GB6/+s+hlxM64x8E+eNu4UbBzFIRNXwe2QquYcMtzIyloDmEbA9ZeUBx9xvGL8Hurgeo6ucraCCvvK3+UgZ5RSErmFjRxjJeK8hY6WblBFEsInRkV6c3m1TydVorT+cVqLbgf87D8+9lLX+Jjo5J/is0ef3bWetIn7NPADiO4yP7gGQBxXqitJ7JNu75ZK6M22z9KU2wt3vAzsIk2c9sKUZEeEROMqH0lHNsjz2qnNiQwZ7Ud/YgYE0Dv7TNQuHO88Hz4fe3OM8R51DM0Iv0Qt7G8XXMbzoOzS86/hjjG48/hn3hvAlTHDamYJKtnYCRMGY2YDk3MffXSHJswSkJqApYZE5MGkbA4GJvJwprYdZ0+6NDcpFBizEwBpFSz4HTLYm47gpBYcNgoA2FySzLELM90+gSZ2FhCDvAcbq/yk6j4vEt7jzmI3SecUbgjNQv5TGnu99uBhg8eIzz4eDD7Mj+DTSVa0l05MWtJC9vmhzFXFyAbh6p8J9o5w2ycsyG6VNiKFpK5Lch6grt6wJYelr2yU//mls3o6JGUa37J6OSa8/J/uAHWJKO47jf77v8zkdYC5ZIh7diiGw3+93xrt99zXyen2GWBchcq8OxBHzTxq2IXvvbOtbggoAXG09bdwkvXofWfMdcK5rePES+wSObq1qrrlGjbAXSeeJ82NtbnHc7HzPi5jGlAzhsvnB85fzK45vd/qD5FfOr5heOF3AAAzBwVuUyq5cUkFrSBlKVHKHZTiVFGtJqYa69pyFogwRtAGYKQwRU9CDA2vxkpEoblup5MGKYRD+DSbFNyc+ckQIwNMzGGBisYr2Zcs5amc4ssrebNcmGYYZOG3HccMTb6d8Hvztez4dFyOM0QxwkGQ6JvBkJmgJuxtsx1rABhWQVTi5PYGYKqZt/0wNshV9sd9ea+YV647p1EkWypxj/nSfYfq24JH+ubCO9X/NW1/2M31Q4cyDwD/z0r1O3Crxvb297sfTjhP95AywICKukCeDDU0WrD+DdU7oybBRYwZb/XaMBO8u+zDRz4nlXYlvgoVF7MwOiG3OATd491/Iu/WvlHx3eZQJQBjdRE8kJuId7+Knzgced53087iP8FnETbuTk+DJvP4lfMX7i8XcYf8D4gjlhB5PlCROP0sIp44mM6JmQPTY3Rwr0LATTerhxJSI5KlKs2vEgSYyEuyNkZOTxXNiHagakQIWCCA8XwnoiZrab2czB84luj2SU2jSZrRmeAATLRMUwUiIvRMFgAwqbPsbXaX+449c4fznv8+EEeRzDT5rZmQpGUAhj2uCIbO+gobrJmL1dKwtkh/MN/efdUI3CGaKHC4ZLrL8zg+u8ts1+B+/sd8hv0sF165IXkHSJPeR7MubYDaJKelTbRf3R1jpXe+Xzb9Nx+1HXb7PMD7jezwTOV/vX/Z2rSLAir6ekIf+0lPqzgEyOiJQCZZpuNvra2UM+6mjy30ovFDW3LwADmFSiAnzTCUWADFc4/bTzofOh+x3nOcNvoRfal2GT48bxxY4v4leMr5pfwr7AXmgTdgCDGOCkzURUUFz18l45VT2tLswqwESp3USER4zOaSrHHEl7n4nLIAufhZnjGp5clQ4RgdTWV0Q4wxFuEBigbBhFs4EyeZjHF9KEkNFrlCKDsmlId5unJ7EaJRh3R/6/DdpBe4FucQ7qRX4DecqUPcOwiOFnXk2TW+YE2fcWkKk6tiKuqevswtFWxWW78EoSk0/kNZgX607rSS+LNqZ39+HyBDsKhE4iMy5gfXDVAvLUl4nPXa1Cy5Wk/sgOII3+nPPxeCTuf7/fb7fbJ+iRKyEg/d7M19997RFBmuD38ltou/zMIggpZ/sBF7Evn72EOd7XDwhDISAqpbhnx7PguSVMn9+WW4nYsSala1EoHKfz/hbnA/c3nA+ejwG9kF+EAQ6Og/MF8wX2xebXGDdwiEbeYDNk0KANMxMNVFr96nu1gSSDmimpQUl5V8lWdkzcE5LLqRUfVEZ2txwRoCWhBzLAM+4XvDsCVJ1sEVSc4eYnqJEUpHQAApiVapwRMNqYniE7JbL0RylJJoZWuX0k7kS72biRB3lQN+jF+C30Gq5wefUEyCgbfDzCzMcw9xguP0MmGOYoXKHi9sXUyvvEI/dTCQRxoYZF/L8ws4rctypx1YEuricavdnv2t2KrVuoQhNJOQEtIbHV/btoDNrjjx/2yc/A/zzPBH+O4ziO49P6r7W3m/3e+/J7rsoAslHLWsmruX2rEnuBM1uGbtxiufYQ6qjtKQdH64ybjQ7NsBB8li0JCTVHJSPtsgXrIjUoBIMM4Hk+zhPnA4833u86zyHdwBfpIActK7cjOIw3m19gN3FyHrBDNOMYNoWRdjM5LSA5rETdmPPEaGYcN4VKV7kUfkCE5blCKR11GLuoUFUziHUCUV1sERQVZuGqDAGhCEQoAkr56BARzOazHKZYrcjwYXNmETZAI2W5I6N674RwT0uY8D0IwWhzjGk2xzh4HooZMcwMjHAPg58yiymGBzAjKjYIyUo61vJqdB23Jtcn/EetMREgWW0E1eCbZMxgjhqoEi7yJulXdlyx1rLaqX+dRKMi4e5dCCkH0qkpwGGmnEIFoQZG5q1nEf5jM0Gt5zi+vLz83vvyb25xaz/6yGtKy8pLaoACNUJk+YBkfzZ8D2wo7XYeNzh3Zeg13Ymt1dMQ8PZUDzMWUCIg9UejiojDGhLQ4g5FQG7hejzsfOB+x+OO80HXIG7ghEGMOW/ggXmTHWL+N8RJGxoTtJxAKZiSlZP7ZyYMkRUyE7SRAT9HqufwUjZQw/HNRMHFbS/XdXmvnHKTzb8iiXORYlPZzU+EKxwKQ0De41GywpKlaUPWEcYUh6o6DaVpNNKGjUFkDxoRMg7R9Dg5AB+0QY5cySJV5L5pKSVIVEBieEjWjRzl2LTEv+tOaJOduzBGGeb8TKpDKIjq2csxMXnoFwMNff4A4er1XXdqxhyoGym/jlU6j8wpLmhIClR0QkVk6YiMLhRXakH7YZ9/tkjqB49w/7G1SEG/9478zmuy7d4+JYMZpVq9DmDVchdw34DPZZ7RmfiqE2wOY2v9By6BBAA5i5hFEwylOAKRLYsXuESpp4kG/eT54HnO8+T5wHnG6QyNMY4caEMGB2E32QvnFxxfw26yiTFlg8YcYCgWuVOlQwAmjZAmWOYbgpkNkFHRa+I7rfPZ503IyVZNxql57nWiG+xIECTGQjwgyRWnzkc83swf4W+MO+RgZDstrRR3AIOZyGD6VcOwRUBiTv2yaTZo5DCdbq4+e4JkY9KGgPa8c4whXDqsqwM8RxlI1bpch1E3QXSDSN4w2QBmw0ZB/rUCgIGDNYFLohTMGV7bzdM4bHnN6iTU+s71p1hqgwsvqqb1TEC3s92FEM/Uh0tqtJOz6kX4Qdcz2PW5/sJ6PB7HcXzwU7SKwDvCk5HRAklzVnwsy25mK3Bba8eL9qm2KIinqBqJz1ryagjmwLH1xRU4VsMQC2Soj6N5pOdp8bDzYedjnicUzFDH7CBNyccfBzg5bpwvGl8y/IdNVeTYXV1pWcqbAd2jiypvJgFmVPCKwvCZwI4KUqjqCAvMEuAZP6Nqm+0h1jkx74YkeSDLv34qTj9f4XfEGThpgJkMjuw+Bo0wo00jXEIOXyRpHLDJYcOUh0aKlOWItQCEGFkUyOE66gGOZsEzYfsFtlUxHqEID7erMGMUXDnS4OoFy+u/yX0D6ASIXZ4FsxN4jWNUUWKf+/vRYGDeO/lChHjlKOVp0RMHeFWh8HTPrSykcB9KfUfV0f7IFMAt+/xcf2Edx4HfgI0fbU0gdry+ZzGu9t3osH1Pl7getD2IY+smrxYwlEZL08Ovkl2sDVWttOy8uopwBS/Z/YueIeVnnI8RJx93Ph6UTGGkxgCzn8BAm5aBv91oN40pDnAIxjFhBg4z9pDiYHMcPQlApVacsAsXUmCrIFH9r0zN0wpOs8ANZHRcdfDtISR7+CaRg70ihAhKER7hcT50f6XfByJnisGqn0CILB0Unk8OTtJyCBeZgX/mcym8Z2JqLGjYgGAmmDnbpWVpJ0LhqjBZyMFMOEIegAtDhmCNrylortF6DrOsA4kYjSLu8ZRlCaABmz785JkW5YfhsrHAvdhVHvN05rcgnUthRA1AqSvJV1xyQUCrvNS3WrmqvtMat/uB1jtlfH6wjt9/+loiox98ZkCqgaJUfNOIt6hBRW1YVeKi+UsqOARAPXiZ3acp96aeCNWihbFxrYrxmYAAVryXlnTZhf7aREgEkB5SIJzhPB/wbPR1gsOqGCEYaQZO2UG7jfkim+TgGIn5mA0txAYSgjkMDVHCDBe3HUDZyVKtAQpKaOhZkWcmt9QJjBGEtSg++rDTI5bTCw8PuDPivL/F/TUe3/3tFzxeJ7yY91l4ILpEUpyjoCrgZpKWKBsaQ7RIZxgKRdpaGhTMYoUMgVCJMbMcqp+CmzHkZoNmfnocDJmR02yOyTpgTz+w4IU+NDbgfpV/rspHrup/IKvfoTD6stcBplxP15aulCG3v4oDlNH2UY5rN5Y6PC/NomZO8bq9SIZyiMUPWANmKyCt0/Jp/f+xtc87+bAr5wHYsrgRZ8Z70KLto5CPqhZczJw2lAvfx+k1vJdE1dnYcl1p+K5w+EIMAHRtoGLFbixCpw1p/c1d5zn9PPyc7jPikAzMHr+0aQAG7CbcjC+Ow+bNxovssCz/ZmGQAOAK0oZlyJ9EoFEQStHSI+cg5p6sw5SkiDaEYSu6zOxAoJi0FqDU9IGsyEYZnXC4w09/3ONx9/trvH3X443+oCmrtNNGkdXIhMsS8ClyKQQEMSRQ8KixjfVNnYGlrlEw1iY4bIw57NAJBo0ju6sJ+OnjIGDu8DN0y2YEeTThRrBhfVdIyr5uW7hfW+oCktbljfCE0QQu51gJ2JUgFZ7DVVDvc56io1kJWT6GvxGCX06oJlts37Gnrz2U6Ad8+LPwe7vdfvnll3wwk/7/e+/Xv8W1MO2PvGaE2PSJhcxE4JpdiOgcXAtWLTuIZnRrc6dU6QaMZJIkoyjNlqxhcSmwyN3L2PemGk1J60z3kBgBP4/z8SXOL4rDeAQHrKF3q8xBnOQL+CVwM7sFJzg5JjhSDTLLpzIzmnEyS6C0qJHF5XSqighPkhAKbkZH+66V+OAyUgLkblg+jHxqdxCEQebBxOM17q9+/x737/H2C/1hcETYmDmld46RA3yzUwGWombN/AFo9ITaBRmyhyF7D8SspjMAm0desakvfv8+xs04iDHGzXUMnIIvrBwgZJAJPKWXOTgGJLlzJLNW+62SlzC2mgcaSKuLS43MkvIMbsY37XOmJHnSu8e4wMPrViWWlud6bneykJ5HSFbahBoZVqUPAEqX3jMefqyVj+H9fk/WdY7Y/b136t/oOs8zleA+coP0jPCc3UQul5h4SEOm1sSMLtYmFFCr46s9mHKPMQypWwAMW0E0aZYswmT7ADBaz5iikSEXkhtzIbgCJIaO0A3xRbqBRyBJkLUTiXiABjvESR42XmgvYhJDswxQ7M8M+VXD0K2abskejO4jx7bQqqbJGntoRUOqiDiVhcMTvVAka5LmF7zc8W5TKSGcUMb+/nj1+3e9/aq37zrfFKchUoV6lSForKFnVoNXhCTD9Mgtyw6BkKzqFhSNUSetr12YAuOY47idNuY4vr58iZh3pM+jaFm9TnYlwHAgDGEoFdCkjbJyxLLIArIru3odLPskMhvKTWetIsUxkjO0BfJVK+qJ7WhCGrc6rVWpg6uGVPjOtp0d68j9GWYFOi1nUw3JtOda1A+zuKmlouucn+u3y93T7n9wiGxaC/YCSJS/H/8FwVYtGMBSyawiW6ECuugj3Q5VMwBKUBn9vDWCBHQ4eInJsKLmAIJGBhJjrzg7hvyIuIWP0ARn8viiahgJiBs4gQM8aDfwEA/ykE2VfkMOKxxYfV5koDXb0n8Iw1DTTEIpApq2vqiL7pUDyKUUgygsCGi5Ow5U24QV2hwZ2LpC4afc/fEWj1d/+zVef/bvv1q8GQJjsGu5jeiakM4ox7rXOS98OwKjGLpCIFLDrvKnMplpAY12HH4PjGHzGMcxjmM+DjtHTppMFzwaeUkaT2V9oUhLTmMaVsZTnTuPsZCddA+1H9m5BixU/oq7t1IBLjrRuh37PXmzsAW664JLwCoOL2091Z1rK9GILKJEdFZRfyJaefQHW5+Azz9x/ZBX/69dc4H4qAepCBWsNq6uliKDrdDzQMdmZ9QTuJ/SImOgonmUin6iPCXij9X81TWGNGBJYsw9DCHCIg7FgTiAG3ELJC7RbcqiKkicsAO82bzJDvIQJzCgYVZ9rA2gUzBygIM2qrjLQEro5Oj0BCgW7p/lSEW4A5KfHqeHQwhfw1tEDTGHAmuMoaCk6p6F6zwhj/vD3777/Zfz13/Q6y/x9h3QGDbGnIn8D65lNmBDVGYkBvoykkT2cYEGKuCJ9JfvtSbMWLVgi4Yxs4mMdjC5Q06IRqNpHInS1Q0QCoGumGQgQsUuMhoWSLjaBLKDYt0v2MxzVP+EWnA1c6mk1BbrrN9ejIMqC2TqFSWgvd1g63JkRlcXaK886IqIqzSau5F34TtX8+9/5d1yv99/7x35d7AWePjB3cB78EtbJ1dW0iISyDkbjqg4LdzZ03HzUQNS7zKNsqEFf4KQwoCKT7cEI20+qvOTHTKDFHhCCp0RU3H4eYTfFDfhEHq6uo2aN8aycrRDnDZvYFl/YRgGMIkJjJT7B402SgqURCqYFpwVKMdAWkXuFeG7KzyDfoUjvHsQHHJEJCohuKoSDDi7cTg34vJT4X7/fr7+er7+Ob7/Gffv9NNswA7LUahzjpGCDWPQVjStahxbWLnSHyFV+ZtllOUAEIAHDZigwjiOifPkmJzT5gEb4ICslf3YJfe8rHk/dHxdJ1nR9FTSshPCzBLFT8HXLAaorbUknbHgvBU8ZHCgZLiW8GrSV2qKXMUKBJbearmW66EtPs/FLKhcQ03dwoZPrhu73AZ/tBrAB9c1+6tWFgBSEu733pffcyUNtHp8sACeReUomFnJXmGK4ajqA8Yt7AKwgvgslpYGGACWLIGcoyQX2JO2VBMIulsHBFKPIi2mhY/wQ/ES8RJxCJa4UCE2JAxVNKaBo6fRVnQPmDBgQzDBADPOoNEstsrHRRIZlozPcmiSBI+IOBE53ssVUrjcw50Ik7s7wlX1SQvPHYpsyGIfnPvp593Pt3j86q+/Pr7/mY/XEQ9K7nG73WyYDRvD5uzSyULbBWQ3QKUhuZsBQO6DhA3BuypipB6J1DE4BhDy4DDOw8Yxji9jfgmZB9FCRuGiSSOLHYn2jJqGY9sEG0AtiFq5YeOH2d8wcoQOwSUQ+5xfVlyRHstW9olKtKqYnCwBjtGNh3kSjT0Kgmv7V5bWTNB9GkyWmpcDqFd+LOsPYHGiPgPb/+bK6kiWAfKGfNdC8UFWHf+lzdARRD5uWdvsptWseIZlx38yrztuzDfaMCAn11ZtoEUMmPAD6sUK9BsZB65nuNyIB6UZMSMOP4/Tb+G30CHSxkyJAdhM6qPZKlyLYIRsXJo8MJNZFoHNDsAGh4ul/t/71NwVgwo6CTkC5+keDohyRfj9gcxq3OGn5Aqn3M8zx6GH4C2DQSDnqiTw7P54vH1/vP0cj1/j/l33VztPG7BhHLRKTpLBkpWUcodF3s9rArrD69STNNiQvFvogIbuqxxrSq5qFCBEjYlxE48xboBla1mlGJnwpYM3szHqHEVQWYaGBEWcaWrBVMPLTuWd3LPPbd8j06yWBMEOvjbTfwUNq6urB03WLdqugrgKSGX9n5HMqhX0Rea+D9gygx9sffCQ9m9YH7oRbAVHKNS+H4zC9ZNMeH1AVeYsIXjr0BSF2BbCgzTlVhqbNeRL6T9Mcelw5cCAXJ7YurKfa8gZ5wg/gBfoBhzSCNFsJr1IYUthLRDVLjsm7YAN2hBNpaRjye4vphDMzGrkSjLTSdo1MyhWt1oWcP0Mf1AhP3E6Ub27CAfC/aFwfzzC22PmLSUJGKycKTz8vJ9vvz6+/5l6q9mVCsxhdjvGMQaHYbVT00zKRi0GlE0ACLgg0cAIgBaMRfsEgNLoVmP0GtYNzEYMxqDmwDxkh80Xs0nShkWcq+ogheQA8kghA0aZYgKULzkjgsIwRlEtC3ZPmOgvmua6vba1TPYCA/uOEqDwIr5KqzLU2CGWg8kB1OVCephE8s6qRbF9QHmRjE7++zxG/2ZWNUB8hv9/zSLfBwcfZ83i8wH9UOUvxcrOCiv74bGW7lmlAqkoeRnsZgvqPmxT3fp03ZStJal+bkvlUSVKkdSeCAufipv8FueUpsKkQQxh0gxMFbMMiiN56+QodQAwYMJggkJMZbdV/s19qE7U1AVCFEOpmf5CyB8PP884H3E+KGeEztPjTFWK1FEIfyjOOE9/nHH6IPMNGb06eJ5n6uqc9ze/v+rxirjH44E4X44DoYhhhcEVsQiI0GkiOQqqGhPVfcCq+go5dkxCRCRoksqYw8bKDzw8a7wgMIBBzIExbd44jtvLy8PtvHvyXvOrLIdgmgTPPrvk9RYaCCblKiGpgEpPpMN1RShSt5SL9bNZfzT8EltEnxL2dfvVvSO0VCCbKbD14m1C0OS6f546xRbsU9ll8aMuctF/rwfp38iS9Pb29nvvxb+zlS4zIj7ggPjZPZlp9lY2UNG8WQ5dAjo/IEkYqJ4HawS7wVXooIqXX13eVVzZeD1+URahZKgBqImd+aXT/XjczZ0gQ4ac3oWDHLKRQ3MNfKS5gcEMFUISkI1BGwk1JHUfIY3k07fvo7VKQQ93USn1hIe7++Puj4fiYTkNN074CQXiEec9zofCFSdOx/2u8+GK87wrfIwJWEjn/X6eZ5xnPO7+uBsCflI+56D8GHOYSUm6Of1ESBynjWk08kxv7PYoVDuJoVnKpkVqcxoDIkYW3SPPtuXc+pTCFwdaIwgYtDmz+fkYR8yb60yoXeHDRrbJ2SjTH5EacGXE5xjLFKcnCocNMzNuoyMa7+tCSyaIqt6EfCXxroXPP4djApi44iKdrftqhboLNer+wR3oX1CPIjSsQ4Af1wF80kD/tqWVuH+kNTP+2oOprp5hPUjY4ve02lgZtADQOKJ6amRF2K6O0KpdhptlTcH2x7IDNPUQMYPE2gVEDPcZuMEOiTSTjDaZsHVD/BV1dsE6kEyaYXNipEAce1IlOxKEpCXhUDhQ/wpAgTgjTtd5xuOEnwyHzohAuEVEnDrfzvsr/JQ7zlN+xuPNH2+U43wo/CHEycd5v99f5aIkP+VnQjO32zAxFXkQAfc4Tx9DkkXoZIo7S6l1ZCW0ygR5qp2tmDwYUoIwbmOCDARTqQ0JwuUMXEFSAv4D48txfH153F8e9znHgaD7nQPsuS0A7YLptxihr1QTctIgFyMXKPCnLvEy3omtlbps44WXsPMlN4tKFy4EKXchL12DkLbdRbYnB1zM3UYaWcVnCMlH4gd81D/XP7bW7fQRi8BWQseNLGwyXg2YAv0IsdiBkqTA3kPHVZhrNg88+d/aH+xc0a1TaeBy8EDXLy1yGowsHMQ0DA8azSOqgT87fym7+D/JujSl1AOHjWlzZtkyY9DWnQaTLpT9AyGOhrDW4YYoyCPOU0nv8dP0QDgiJEd4nPd4vOl8pQfOU49T/ojHq8UZjzf5Ge5xRrjur6/n4zVC4TGM7qdcX798MYyR43w9+Hg4aIDTNNxrXhZtmIEihg2alYCOWSTNxobNCZuEyAr2VVShA4V5BGUlwCCZUWYycgwec9xejtuXx/Ei3YUgRk1NQQ7IUfiS/geYnKaEqpDc+zx1UT5AHRzUbWNPIoBFGMr+tL8Ye+8heWcbaNwo76IM8+ke2z0VAN2XQ9q2pnfbR0FJxt/+9XN92JXZ5AckAs1s7s1MPKFkbQ8wGktdATsS5aESKEEHzWY1PiUfQoUvJBe4SOUXCNwyoiQBk3T6WbUDUbJkKIYrxweWpTHUUCekFmc5m2R5kFLWBrLjq2imiAjRJIXCRn8i2e4ZZkbSyUmQohR+nsX0d4c744TO5PxA+fod8WZ6xPmgh84H/cT5Jn/cf/1u0P3+dn+7nw+/v70252UoYFIOF47TMQ6YKBiylvwGKIGfJFPFIzlaJnCMCUisUTY2JscQHCPIICfHTB3QSBqrDoyTqeFfDbYdL4+pMWQmGzZv8/YS/up+LwduNkYxfjxcGis9FFZKFylXgUKUfKQ6aaoSFVJ1QUbuvmKIUtUu2s9lsp/x/SedwUYL2RJ8qTm4hHytqiBZ2Clxw6cmvpYJyoSm3/Oj1YA/19+ycnbmB7T+AKYau9/LtABa6BHY1PzLplM7ZrTKxjVCABuQojVq+JrmsQABVqswJCfABD5ooEVYhGUgnkrMAmymoJsElvjOaiVFZA231F/GBBCrlF113VSxWRNISrSgsYrI7EGixxmnZwFA58OyKhoIuOSM0897nHfEKT8VJzx0voW/xfka95NnnO7376/n+XjcT8QJBTiQ0kEUzZBjN8+TKVTgWQ8fJMIxBrna7kYJgqYCBWxwDNlgDI/JOGw65q3HFlAOcihGMGhjdM/FOusiYcPGTccXmzfOaTYBhud0Aw8Pw0zXSc600jnIhxx1YQFBLll1/LEvu7p5S9kK0GASo1sGcrwxa5RCT9rpAsBOYmnoJvXGL+LmQv5zqt+qUZEbE3a703YgSysYeXfH/xBrjTn88Q7tX3RdNaoPtiaL4ePVmVnPU/TpUOX1WE94ETc3zwGgK3EqHS7Ve2jJKBkXKLx/fd+lIQR5cgAQggpzH+6FawuthKOmEPVzHpDkYhAREWO+lOlcmBXyHdUui0ugwhQRBFLqUo14hfw83U9Pjr8CCigHlYTCFS5Fvk7IKOkR/uaPV6TTePj99fvj7dXP0x9nDkgYdkQGnWYiwqWh8zwFDWMQJrmCCFBK7WTjHNVVndEsCMRADI4RnPSp6dDBECzOcNkIDHEktd9GaYEJhI0cbim5zUOH83HY7TZuL3a8KKeehZAMUD/nMZjCpVg0LTRpx2iN9nTeh2f4J12Fh0IemfSswkCzcpJHkCBeDyACrrJt3VaJ/+T7exDF2hmsilLeS1WaqloDsoyxJxPbNn9AK7kyrc/1V62Paf3RNYCEVn2FVw3cW3FnuOJHkD00RrF7BVY8XgmAujicoey7WsL2hFvDuwYcCgEWbufJOE0aEYgU3EdOhNWF7Kr4S1IIbqO4gNWZoOaZ5NfQsoBgF/unDcsFRyQuLCWvFDmdOL1iCj8IKhGI0p6JRIcc8vAzHuf99e18O1+//3q//1ptAZAUkBtqFFbNK/EAKXc/T0qYpoDrQRMZIjns9HKwSfNPW2ZjMmbAOA56uihhCND5cNgNegBmNtfE9WwaNrPIMvycdk4cL+P2NV5+svnFji/D3x73c06Spf8fXkUHNDLU+4KLkNMllvIGEQBCmtMEuDyztLwMrRG4IvTrGrwDf7q2rCo4V30jCQvR9YArbyAXi9RIc19pxEUzzdUzrq+04EdaH1zb8m9bWQTWh2yfLi2gtsiJ2+58DHbWjwTiKwRvO05aNtq8C9lyuFiLBVyx//5ObThvsnogk1h9V2fqbxJKhxRcA86Nl7CoXDoFl2h2oHXnr+/O8NesCIkqz1BMFBb6VarxyzNIOc1AlWYE1UNgwhP5DmGA0TXtOHV/vT9e377//Mvr6/f72xuAOQ8zk+ghKWwOAorTT0EByjjP+0PjRAwbppDMzZRcz3C1AwgpzGA2gmZj2DjgZ1awJSooh1IjzwZL8wgJbtmsE0uCg+5yKgiMw8bL8fKTzS+013E8gHt2dnQnXEUCNAQ1CLOsyfTTIoRHUoPXbUMyciNpzYVsDkifHcUh5nbPJJIT68YA0HPlrFrS6pZLE89yq8UmUkf5BQGxXD8bYrpYbVs28AMSgdxXGPfhbNnfvBaj7AOWAWYpfvbDYGbuajkddJG2kNWa9mtQDs9NmiARoUrwoRR2XghSP6vrjuyqnoTmXY2ReE5uCucJaErmZxA5yDyjSaZ6v+KsQC8KYGnJ5aiZIRmUqoaHRLWYJupfR55M9KaWUBkdh6pzLWXXEICHnylekAgRSrw+ACfSMcgfcT7Ox9vj9dfX8+H+CMiMlCMkTxaslVheZCoTEW93RIxhcEP4nBYWY0gGDYsgGDW/ODtyAzLRzP2hcXJ4nGEefvq8uUZwfEmfiGqaVQPsVtUbKsKVgyENmBPHi+ZXm1/Fn2m37D5TMoZsDpudTqWLBpB8/xXzb3Mbtgwvim3fkUQlesjBADm+TM3g3D+7VjNIkw8aETIbLLLp6gBAI3d7WofdvufHO3+twkNv5wd82j9N/9+2SJ7nuY8Y+ghrZQAAdlkVbPSM5MeYrsx9MfAIkOAwClGCP9jvvz27v3xAbjyH6pplMK0K/0XogCyc1V8ULROJQJysLJ5FbKcAUZ7SnEYZ1e4rSwvTzGDd/kPSWpwNDRvDc+DW6Tl/OG1eokCeXcFl5dR1D0k5Ut09PM774/79/ni7v72++cNzHgmrZILlTcMVUGqYSmGkn6cCGAMghDHgpx/HoEbqXJwQYpix22bd3WkmOPRAyrL63RGcGfePcI64YX2twlvBqcYXZG8xCDswvmB8tfnTvP16v9/Pe94TQzAFzzPmYRERusC6vsQ1SlIFmi3KQD09dcIKu7Gs466i0YrWVypwhQZaghCVNa5Ifxnxxvrrqxbm06Updf/wQplWtT+jgZ0U9Lk+V63FQfgg2UA6gCzYDpIqZeArjojwpH4n/g3AsJp9V/yV2XX9Ly5sxyRfCi07DoSMS2u+4HpWAwVdgEzNTQdHKCPuQUrhEMGh8FCUJkGqnmWHGIv408yfGqVbgEBqJBCgXF7lyfYwhZbXQanbHoAmFGUJBIBE94jT/Qw/HWKc/nh7IOSn9zycaj5I5WRC8nCJGFUkN6qmZUWc6Qc5DHA5fMDWcE5EkSczcyjVIp02aNn4+3gFDmDKTsHiPMNPxqyTkGcPzBMGkjDaCJrssNu3+fLHef9++q8iQzrdh8NFl1XjgwKYy5NZnYRgqX8r4+wG1kuHZ91nCSQtpJFbVWn9X1eegCsEWwjhMtZmVglEQzrY7i71K0/jCtZuKPJslMv5NP+f690ieRzHx9FTmnv6vPpinsDcCNTQVi36nWoWh1yeqsUtIYFQEJHNXKSRY85K4XPjmwb3etpXZZdXBOcenl2jJeJvpqhesBThDzAEISwtGxERbhGwgn0TCUJiP1V8TQPvxDRaCtbkl3v4En84H/eBKNliSCg1sXRbSbQfc8bjIU+Rf388/HykngJUPCG0ijILPiPAkUDtGCbLQsPAMEfMwSgkTBZkDJqGmSDXadnSWzuhUsswr7QpnPGgPPyEDYTHeWKecudUIKQTGEgM3SZnmI44bxxfOM5x+4Md/4V3k2DjILwOIQm2oQSRpOSAIbxcapLE2pAnaIbstbtqPCj6VrfsDlyXX5kFtle4YoTtDVj8ojHQOcTT+9cG168r1NjyWuVohapTQ/aZAXyu55U3zO12O8/z996Xf411DYQxY5l5ABuYuz9UjZy2u8iaKori0UyP1PMJcqTwwwKK8zEsGdHG2tLCQur5VTnf9ixkWRQ8ZCQ9HuTMQR4ecHcQNowml8MGpDgjLMaVlYAlYJA/s8wJWkgA+eVRdUmAwDCzYyKcChsWIoKqPKlacKFBZIqD8/Tz9PB4PM7wqOSg9JHcisdvgyNPR3AdPsJh5CNiDgo6QzmJaw7jgTGtvzdh9WQlMRBMatFd86BxEAAi4gEfyJYxBRSZgbVcNoEUhY6aMTkOmzcbLzZf7Dhk4CCMAiLknvJEBjBCEYpIPC4vJYHqapAYcpSiX1Vg97VCi/QAV+LIher0OzsB7RBkmI0IVytT9XvVaQT2+zPDEGVAUX+N1XJYCYLqFvYfsQ78uf45K++6x+PxQ5LEfrsmWfipFv1ODdtWJ9LKwZm8eUA9WLWCcTbdPl/MCVjgwm1SdMfVjzee8gCs9uAq4AJUgPBwwBBODCEFiol0LwnHK0cBXOWFZMukIFpjwYICEcUNB5FgFxg16tjAnFkII2GUKDNlMwRtjAG53DKFoKU/yMZpeejx8Mf9fDzO8/RMGYIMz2bpNIgYY0Z2VJyaZnMMs8gxK4+Hz2lBOJCz3GlyykJwSQh3tRJyd4UJBsaYx1TQPcQHbZgdgrNIqxERiGCcihx3LzMrqWsBpM2JecN40xg8xvHl8POI8+7yuJ9jTnc7HTNGhEeYNFCQD9AVHpbGRo6hr5aw9Hn1Hjac3/mmmUV4R+hoX7wGge0rc1BKns0Ny3l0PzD3GJ8pKk4IQbM81FBkHQvVEL5/6Y+zzOw8zwWafa6/YXX96aMMCZjuPUel6f8VPKJBZwBVwt0wV8V6dLLcuqE3iBqv0kSUXFrmVcvZXPl+l1hXSVAR1T+cf4jQqvk+VSkCWuo0ZA2Kd6YdxAASOIrUAiOGQAUcDuPiGqJKyjGoMHgkYmGo8QMRWXyQCHkh2kwZt9P97e3tPM+IOD0E84SmPEPQtIBKDIeGiHAklVQYFn56Ki8YbBqocCcguICanFU1hDDSlDUFDYAJ8kdIEfEAHuRNDKBmT5ZfblYoqSSbAjaPwyXnG8eEDdgER0CuIAPwM5jWsjSWWz4j1YaquiBAqIkOK0rgxb5XX6niAyQa1ywAsycW0HMBoF7MaXGSpOjhMNfbWHpBXUqimUFMpQoLSq1LuG7eDnQ6d/1RVp6BiHD3nHX1uf6GladxjPERUKC5AvcLycWlC4Q0eT37t2x/VtKYEXsJuSkLbLkdDGPJOtugFCpOD4eZt2utQlyl5zl2TFAAFgHBhs3w9C/ewTwSeE4eoofCT9CQA7UmmvsZhCtOwYApo+TQmYG/pEDUdN0xcsI7SEVYVjVZgzWARu+J7MwiJJ2S3D3Rq1PyUISyFKxAyGuWMpi9tUypuXBYN0dQIQ0ZFMk9dZhNiwgmWxQ4IyKyzJuJWQrKyUizwWkWcT7eJmS3GwhYDVhXxFBEnItwa7CS7kkUJodJRggBI4bBRk5ME8zDaXE7Jg3Zx9tAYEinYMqpACnRRLpCnhV2xIberLXHpOyuQzMmrbPpOdg/p16dM3Q+sTUGr470qj5V4JKfb69DQoZuC0Tfcr1rP1Savx6ozwzgn7k+ThY19+AMJeRbcVEBpVnpHdlWesX9lRPYnn0vhIeSDbMILy9CFFskioMSSS0caZRq8mHWIE4PlxGTjdpnK1gqISSZp+vOCeyqIYGowDROxSme0qA8yUGRxQkG7BTnymXqIEOoMVJNdRwkLR4pJY0xLDIiF8lBm4EMEGRmx3F7yyZMyT07gGtSrmdLGxmkzpBxGMOIR4RBshx/RrhDYwzmsHQoUsRNFDWGRZw5ItKlMCeHGZzkdMRJTaS7VgDyqO6ItJxmyiOCLYdfsXkVYMegHeK0edg5gexAM0nhXRliCOU5jcMsZRliTf4ymrV2W5dqL0Nc1pwSljBcvQX4y8/bO5R/Ge4K9muwz8on2V8QFZKsrQMLpVxoZ//xx1nZBvxxjNe/xEoIKLOo33tf/jVW0UBZmOhWJUtGv1pspbtq+vYqkyCtZ3tRhrBKCQTR8wOKnVcGggg1IyfYzMtyIcJo7YCEICAqnMxR50Brj2VyISCnJ2aQeuJhPBEOOi0gh2YbAwkuwcZIyikjOiQnSpqmtY2rfNzMc4CojgDVtBTasDHGGGPOw2yk8Kaf7qsxKg1QRJVP0jaOERGSmwGYmClyAyP8hAzDcEKDzBZfyE8qVfprWrBxUCcI2mj3h9RnHYQMMnACI80yjBgIXgxINeU2p3bCRg6HIccYE/HoS1b8nAhX6pYaruZvRboJAIKpg/SUlDVr7s3FDa1AYVmobSiQ9h/q7PWt1aLlFzcBQJe16+rowhOBEDsQzisR78zi+1TgR1jd3LB7uM/11600YmOMj1IDEGLJ5+ZzvcqpiagKMuXDptAJgRzbk4UCcGIVPLme84zKt9w+tE3sS0JIdVdlMVZKeMcz8RAAS9bMoEWGj6kOIZcY7slEVAgciiCDhnAfEbSoSNiCCpgJSpgCEcOSgJ+HkPL3NTUgf802MJYekImZIKwCt9mYZvOYt3McPidpsXQs4DYGSlInIN3f3kjOYYm92LAxSq84PE6dGBkIBwwY+X1htBwhkCG8JAxL4200wTmpoDtmMGX+aTeNg/OADXLkEEjQUOlaYzQVAcsMNofZGOOY88iCfYRj5KWdKwzPCZoLXVmBd26oYvDNlKP/0Ddbded2I0V96p3F3wCf5JIl1ffq3EaH88lHCml0YJA5K5kagk/4/uZyGkf84SZA5eF8Wv9/ztIHE4SYrYaD5GBAVRDNkm/ZchQoO2xkRS6f9tTAqR+eHnVk1Mt6zq86sBolLugfoFmiKolvGGipDBNC3dM1kITtKASxCDxBpi5BjrkCJEszncNb4GbJhjwLTmkMukw9DBctPb9AaiLUYrnLDBockX1iIYkAaWOYjeM43mh2TJACq34QobjkgySNYad7dr6ZE7cJ8e5+DLM5gpaoT5gsOMdAlhOy0YuCEdJ5hllmPO5BHQrXmNmJm8PuD40DNjinjWk2zCaYziApOQmJgEa4lNwnisZQeHhdU0Vqg6YPSAAouaHDRg4EKyWNJgosAyRdc6HXiwAWVrPephoHVIZdXa3FuiTgzIQpL3ymoiygTghI3gp0/RFU3vVua881agD6sWig7n673b5///5ZBvjnrI/lAKSkTSTUkkQfle5bUyYKESHMOEYCvhnorUEcxQCp/DsiR/t29yYlMWA2ulTQ6XzRhLq60Pj7MD6QrVTJPY2ca5IegLhMKxDGkaF01gCkMEp+wtxG5hcpDpzzzVVJgwKRE+OZwFWKZWaTQg48ySzoCmVpZmZj5MhExQCTpz7nPG63L8ftdv9+NxumKgILTPGFYVmHCFdQioAZYQaGUSniS0MyKT0k17CRoBaT22osNGlgjGGTiDjPk+GTL4n0GydtcE7NYXOMOcZMHzAqagaAKtynpsMSqog43R+KyJKJ5AE83MGkPqUUEDO+ZnllLpRrJ++sIHTPEq6LvpUHVEzZmgpXvSVlzXtG86X7r7WRul8ygQO2DhaQw6zySuF6fSEk698fiwVa7i3jj09Z0L9tkbzf7/gwbmBG5CBAAzxN+UJsuzh8WfYGdq5HCNtMjwX+o8DrnCVg5Vp05RIZ/ueno5geVa/MDuPkjHrFc0GEwpGCChWoJ+DN1FdjZKdv6jO7/ASdWRVAhDxlDNKJoZsUWimgO6QiiiIvGej1jkEGKBKCQUbasBGm4BjzcLtjjOPlZRzHy+3ldXx/6AEhvLSY3dOTwSNIDELAoIULclJuOHVGQMN4DDMxZfJEg0ZlP9XDQDJp7UEfY4SfSv2iUrGwTqvGGMPmsJFSF22G0/1GaXcWcSicyJpEnxxyjEETqtJbDb55lROHK9AMJEyt/YnrPrgi0B3hyb9EXIWBnkHHml2Wt0d9VFpdh0scrv6CblXuTeVZqHGfiK4u7GMpsTknIIs+P84i6e7HcXwE/uK/9NIPFh38I2vWw1MiykLT+VcxoKiDqbCjyF7Qxn9qK/2YFfciCSbDRlGArgprbvAyB2lyaQYEE+ooHpv6C91gyIJlflciE2XOsbX0pJ+IiAc5KadOwCEHTOFkSIEIjoxX0VyYRVJqISRU+1vNDOMgMYwnA8MAkykHJyLGOA4bDySr8TjmnGOOt8dbD9cEwGpy7q8EcMoHTMAwhsvpChmGGwAjw4gIl3JCL0YVf8kWsquTHxHucg+PbO91aVSqYjlWfoxRENmqzLNkOhKfCj/jdHkgEyu4GbJVGVvRmNXShwbkeljQBSxdFci8T9L4SotudK2VHOzS0Op0kE0oQNeI1PpCdSLyL339yXZp2Y1sZE9+f6/rsmhDtk7iD7JWpvV4PI7j+L1359/xIjnnzFTgx14TyPYoMGUVYM14SXu8dwhHznXsWK9i9pXU14uqkYBIcLnQ3nDQmPViro+skBAwhVhzqVK5OYbREa6Efqp8uzTGGkkKBMFohqMzInjSH7AHzjczg4YR125HwOZyb5kBXAFsWsacItwqdEY2AXUaDDirmm23eVPcIg5/+fL1/P42by/8fg/PceoJ4aRwUfL3CYhmWaWYMEhGgxGGCI9gsoOCmJZEoCp3MINkGFADtrJgTsFPhwsud6WEW8J1AAWTYLB211xuSUDKmUJwV5yK83R/IB42zvTZx7TbbdrIWZvVT5xa0Da68pp4TXf77kKbOTtoKfjvdYK8/56JOQUDosbTd31Y14bST7cgXNVrAkRou6vQoGVewmuvUr0ugSE23fmHWYla/GCV7d9lfRDrj4KAaAnjG63F0zPoy1g+J+YMtIJj/mmlAikFs3ACLjrNhflWigDQODzTgorw0RFcllc7YWjGR84nUFJBC64p6n5ODFbkfNmsCAZHFgwkORSIU+cJej71Rlb/V+9PwRiCWnV6mCk1/+EAQLNJJo4/hkBk41hk39jAGDbn8eXl5f7ydptjHmPejuMW7qc7gfBs4QqYlUSenAINLlfq7E9OCoAXVg8zhjBgmVa5VxOyRrd8DUhQ4DwdD+fhcT7i8ZgzvxERJWi9AeFVaiUoWMvcSQq5uz9cPqbJJXqWuOc8aEZiplZRG9Zs4Chh8JzcfI0My5WRehLDKodYCZbVjLf24RH94vJP17AXACzpuxKiTqouKowHkHlSVxdYhR50gLKKB+kc2GnERU/4IVbK2ef6vffl39nKNDFHwwN4PB5zzo+ApM2VHUvV1aVM7RsgWNMzuCl8seTMrJ/YBFK4YTO11Xy72ZCQMSlZs1rq+a8NODKYTMOWo9KpRDxCktx4dutmibpkBqEQR5ebAWMID2ggTvghBi3idB5F7ExPVvMhUeOLCQZyFmQdgsFEySqVIRiecxxhY8hlNjyj+mPyMTimzXG73eZxHPM4x91LGaiSpaQ5pboGpdLzGcPPUyFMDuN5Yk5KFh6DDMRinloCQBESnE6CNA0XqHGWMEXL3rk7I0rDIRqPaVTLAUWZfjLO8+1xf70/Xh/+XXpk21rbEaWPJ2gcVQQf+aer28uKObYP09B2a6lDiqsPK0cBL/Bnf1vXeNUlqAoJyltVWeDaeMYu10bq+8sbdJtEbZkdeuQ//5yH59/aSvUCSXPODwJh/3dZ6TjP89z1Mz5IIjXNBsEqfpasm1o2kSt+RyE5T9RvpIZozYfpd1ap0cCoJoCi7QirzwyduwNp01M6ApVy7GXhtBgBeETBALmTVWpI2AHo3EHSA5BxIDyHcDGEcERwVB01urJI0KzYn15KDQWSI1WkA1kUpWrAMXHKT9EwhsXQGDbmnC8vL1/fXr7eXx6wnzkSh6d7ZOVkVSNdPgoSZ0ScOoMaJpPZMagcpFwo2hlOjzFmuaxoYyecZwiPY9gYc46pPqRU7wzFw/0gQETWxpO5CnqyfTPwV7zd7+fjjEhP8AqcBIiuISSNyIaN9CBWUL6ycpNXn23uE/NbIA+7XHHpCe4hP1DXmpcOVa71QawtrHbeqgTUPaWqXtXwHXV5QsCFGnWrX/GJs/ftxzOQj8cDPWXvc/3T17L7ETHnTMrZB+kFm5A1nTuNMgAoAIk9H3jFdKrRehXolW3eOgNoVsBsRborLt9xgHoiW3VPEqIsCLUe+iw1kmbISQAcKZtDq4ZkgwBLfbeAnDJk33EE7TCTh8NcOqmAAuFg2DQgJ6pUPTGtS1uvKJm2hYMtUEbiMNPwCNgAhmEqfIwhs3HcXr58e7292hhJeooI9yBBs/AkWSV8wTlG2tBORrhaLxQSaGOoFNwsC+OQR4oYjZnjFuJx5zHg5+mPcZ7wDPkdhEfAnX7CTxtGGdnTaKQEsBTxOM/7+XA/47zLX4k7GbSCeFhQHiwb0szMNkQlu4B7UvwqFScEk5NzWKVsrUrscgbRk7hXYQBIVagrll9eU1K1bdT9lOljRxcNJbG/NhkK7YQoqOFNAd1YVinpj7NImtmnA/gb1nmeCf4sKdCPYP2R5Mh6JjukQsdcVj1TFYh1jS1fzJusDXE1Eqe0Wc1qBBkerfaTH6xO4IwuO59Iv5KSvZUcmNEMntT48FUqQPM1AQZEDhbFFOGPEKZhKR5ADnrC/kgHsL6ggJ4LskiiZI4yYBeaBY5hfZhoymQWTCxksGFjnryLsmFjjjmP2+3lzwItRdPApB+FRtk7ZgUFhIkuzVVWycnxUdj6HEa28DaQZ3j1x5kNoHrx4nT4wxTuTkWox9affo7TaDZNBfeVGAeNOdAs4pTe5D/7+WfiFXhkwhPCGGOO20YoGg2sX6H69kN6gOux6aCf2zu1/ZUrhsDTtbiyz5U3kC0KDUhhpY9XZK313ezm6jT6Kl4atClD4KKV/mg5gKRl/Zej/Vz/lLWGlHwQu7/W7Cf2ksciwGRwkovetz8tyarQk6hLBcudqRc/vHV2QGMJCQANRRfBg4S65ysx+vQ0RhjlUMbAJTXR/QQKiQYTPFJmusrVOhkDHO5BPDhviAd1SCE5lcGol2RoqOYUGsOdMPSAsDwPY4zqPcsxKsw+5cmIpCkBAxwck2Ya4LDj5eXly9dvP/3hvN+Nj5oEL9AGSsByZswaoexeSD7LA6dZKtBajVip8oHGGGkYM09JJQnWLMnU16sBN0SEn+FuSSdy1+lBN8YkxzhUgFT2/Hq4Q6549fgF+h7xCj0GLac1pAVO3aMEz/PaLcuSVzCDhrbdXLeTKhPkcg/rVlkff1cA2I3+9nqTi/c3s8dSQjlDbXM9dRtq30J2Om7beNqVH3dlpvV778W/9ZW34gfkzs6sJWZoVf1ZBIlhYz0jG5JbH0uLoJJy49YLlitteLYNl41gQwrrwWThJI7LFsgMhFFM1QEgULWBogOq+SxMeLsC84aucjaAEH4OO4mHYBFGTOhQnLADKnqPlPMGEJ4NCGmnWPQkGFqjOLR6ewkzjCkPGyNpVOM44jalOW+3cbt/+fb1p/tP99fv97d7xHdCabW9dSRYO67X+3kbww7DSIAC7klhqnQqMZiSTUJXRmlrLoJcFojIgnUsVs9yye4aEzUczGKRc9xPKQIByP0R/ubx8DOQjtUY4all0aYZ7Gu3jOhWEJIaspdUvSVNCOvqEdZNsqJ2Fr+gGwN7XYgSgRrdk+ChXWlBF5CNFqXm1JGvnvimmztB389cdaYfclXBqWktn+u/vrJ3+gOiZzkPoIcOtq2pZzZy/OLVR7Ng3ELitxcAkFUNXnBPRqzJFqlXsbr/r22arUhQ2czVowMxjK3D3BWCevhNWdyNkFJ8kwNTIVh2HXj4ncNoBKb8ATyAB+3QSQzBZg5sF9iywsEWE+ZSil/1QyWWNESKok2dWXy0cRx6+cJQvPjxco756zyObz99e/v++na8Ph73UNZd18krO2ScWSp4RITJYMbUCLXwgriGUQoXiKDcbEASLBQ2l5QTc0BNGmSPsyaCecxZljanQ2bPXbqK7MEOPUgMm+Hh4VZDbzjmsqghyf2MgJSyS1iVgMJn+mcAZnmQwe3+SLJAZ4151WKrE2AVk5v5k+8vOQ1IEU6Mqs5XO3S0cBNXF0JnDJeDsYbz0vXm+1v38IcFSfYU6gc+zP8u6zzPFVX8eMDgf33NCsb6Ae6RraW9jKqeFfLOSwCuGn8KQrmgm1o1a5EXpN5GXyu4W+qhoJGG0n3LH0PAGNQ0d/gZNZxKSvXhwnyS+iJQhmwzC7ii6f5n+MPGRJzgieGSIx6iFXHJZgEUhIKybgdT4ivGlLJozCE/01r/hFFhOYF9jAPzPL58Od8eL9++vn3/5bjdvn77+vZ6f/iJ8KKZFpTTobKUhgiQe7jhwEjTFpGWkYhAAi8qoaCkyIoIxxwWCne38Fs2aylyUFmc55jJ6E1/HKnnoIJmZMgcyhmeg6QMkJ95Jo0jxYogec2DDAWDMTiABarnuVnSyleGtj1LGVgMybeUoiKC5oPVwOFV8ACa8COIhYtl4b7q8swB1FnzN3ULSdYJ9jdeF7ULXPklPzbg6+5biva5/mtrjVL4vXfkX3vNip12MrXUj1/+tiIyLLgGT9l0sXpWPLifRzYWsQDiLbuv2/MyqZCi8JkkQ9J74Gxm/JE1XwYyiiWQzbEGGGERCp2DZhZkQBH+IE7SkSNiwmVOVMtb6l1D5YRK6rmxgUKykDuv7Epj7swYktOMGNIQT5tz+Hz58uJfvz5++um8vz2+fvvDH3S/319ff23lhd5WBIFh/P+3964NkiS3kaAZ4JHVPUPp9u7//8W7Wx01012Z4bD7AMDDs3qkpZYUh5xK17BVlZUZGU88DAZD5elmNJ5zRmQV2+QRUc3YyUJVzOIhUaETpOVcnTkf5x2Px0jmT+i6PJA0Qw/jDXn5SFCmlIGbQo5+n3M+skpjBlJulpCYNdiCpnZlBdtqDIEgQ42+WSHnRfgBsCY5A0tIKvsA8FwYSJeQpLLsPzDU1IjSesK6u4q4lVRU1ceavwCyCD/XDZxpbuJ6CTImFfgPaxwlvb29fYZWpr9+febxmathJOt5zcdfykAILBHJ57VkdZfF37XkChnYBMoXvW+lEfVrBsOIpbqZIb27EQwzmswtAhbMMd8l0NDCxvlZSVAkT12acd4bvHHzCUzFI3CnHdSAHJEIu4lWgXnmMcbufnvaI1Wzq8GTKpoZyqTkfsQRghgTx+3L1y/z8bPOx3k/533+6aefNB/njKmIOVmipgHAzRsPqkA6ZuQ+ZP7hPXoFgMRQdi0kDcayWnue8/hiJB/z9DhHzDinS1koYCYzeVxVtemyTBUMFHESIiLfnFlgqTdUr2/7wvTYXOi52r0rWgK628IThq4SUX2sUKCnMi+gHP7cWNaFDaY2K4HUMiq3zdIBxNoD4vJA7Xv6G9Plysyyu7yvphQx44+M+aYshKRXGeB/uVanzh87KfxxDSwMJI14yiCgcu2O0Arl6aLoZcHXv621ksOvyhOoH0UV+lEOY7GVqyGg+kL7zUniDAnRiDOMlBW5XgLpCU+lxRAjIiA4DqYLUFBBhOYZfJg9zG4pCq04zRxCRMaEHkE6ETQfYEpR1siRhpIbWwBgqY0s0wELwsycEYGQbn7OL/pZiDnPOGPez/P8+Tzvv/z6a5nePDSF0eaczrZ+RgDnDJqHeJ7hA2ecKUCaZ89pqdVtxdKXl32tnqwMhJkAf9SvOT1h6iHI/IAUnIqHNBUztT3P8xFzpredM7LzLK9kOSAzEKFQIGX+0qFb+wVhV3C7jPgeHDT+U3lF1Py4vZhc7b64BglcSoR9M2IZ9w1ULMUky+k3eBKSy/1juzSJqNE0f9gMYK2X9f9LVlqkz2b9AYzhqQeniNmZe0v6VMmskmg0o89Idy8S46b8vnp2Vs1AHTZDylm2WxVhhYEVALYFSMsLVJwb5shxLKLhJIwzySGVf+QelsUWJiQq1QsUinneB2+YD/OTmGyAnBA4pIgp0A1epcssAWcPWumNBYqGSLgjiBmk3Bg2oVMAxqGY8OBx2Izx9uX209c45/dfv72/fzvebn6/2+MRzWkCKFSjtBEhuboVDVkSmMrxjqqxwSiJ49JfAkAwR1KSdDOKCxpTFm9zzxHVzizkJJx8T6UEgDvd6cMxDSXOir4rWkROyCnEKdpxmU7BaSAig4dssn0qPO7xfv2cEM1zEgB0+H5BhRRZk5bzrXmN+s1b4Y7V51yCQrQuKrDLLQuovAoBf/iVTa2/9178c6zV5Ph778jfdY3KeqRs8Y8Zi61CLvUV7CY7DTRbwmGdMikjzp2PvfjgWn0Ae9JQYaNadg5oU5tFg1l0IEOJtUGkuVvm7uokobpEFVEqchHzZFI7nYpT8dC8096gkxiShRipFE1jtrZRMWGubAhg0wSzZgnEVQ4AFZhTRpcpQuZuY8z5wHDeDr0b3DiMw+irKYk98CDyGFtataqSCU/EjBNyL5lupuO5DK5iSsPczY2eLbuX4L8AzJiMWQrUCims+iRU5jt1n2cYiAhqGsKISCgeZDEvS3Mpu8+AJNLQyKiR9ZbfHarOW2vLruJroWOCSzUWl37UlSKoW7uzaIs+FlaTeItTLwueNj35/+xZpEBeqqozkEgFwB53qgswWmTQP+bK8/HZzNlfsz5DOvjjGmXZKzCPRuoTUzUymelXRp+oekaXLAC//tiBG816TFjXkbE5iQ97UK/wst6etELILAd4tSlBylbKeOSjX8iMms8JKAKG0IRgJiAIxXxgPmCn+QNxQpMcVQEuDR1TRYu1e1EDgCMxDmHzZBCYIxh7VGSqbpqZe7jDAmYByAjPbAJidf8usgqQI7lSBVMxY7IZqQmggzEjCDPOCGPWqof7JfR/HM7hNfKlFRvoxhZtFiJB8wr8SWmGQvNUjqyRFCc5zaqtIv+rQy016xraIpWAXlJUS+67g/MEeS7/XTa66wZKYNDXvbFuhm4L99aMKmXQhvsu+q8Apux3cxEa/+9KdXkdIGRc9fz+KhZBuXpc/rj2UdtUztf6C9cn9JcjSkm/pG6kDOrzyUkrXGZ9fWbF+IsvGTMUMTrflLiQRxXqUYvNH1fVCVm5fiIvFcaHAjkBxtzMgiYa9Zj5mRwsDCNmI1fqUi4BaGpaNveSKUVKBDSlyTgRM0dORSNchGpIZLJHitYK1ljI7BCTGwOoqbRugZBqUmKk4DNB8kSIdB9mNsao4nKdsOgkosCJ4b7h0aLR2rDmCVPC7m4V/Me04Uv1181suLvlFn24H4Nj2HAa3Q0pINoRcmgqQjExY57zvN8VU5pZGpnnaZVzVKUU2TUQyTaa7uUYWlpUkWxcaFXzUSynFUysF+tPCzPcc8cKxlODtW+VzG2QGhiNLho9HXx1Z5djTvoy2HMOUFG+qTpS+rtSBaRnVfx1j88/7iKZ7dzPUNtrvdbTyvEmCXjYVHaNq6O3iprYvVpzTvLJT6rhiQUTsYSHN85oR3DJwNsDun4Cg9e4D9SLlDJ3d6NNlWocQzBM5pTaFd0lJRxI6QVkG2RMTrgZfVoOCYhJTWlWM1Spf5qk0AkM6Ew5sgwuab0/LTmUVkoJuAOaptL7sYARDpsibBCGgGwMmh1jlBLoYsqGaAjpDAzWNHd251QVUWYc7umDRFTB2rxRb5A0HzQXZAYfNoaZ0cfhx5sdB0ig9E0vhA1Kc6/5UEQKwwE5aq3SFJcZLWaScglwnjMOn8EeG5mXG+BTLzdbfnld33WzJCkoX48IdGxBslK47dbazkPXTEgpJzqUP63ZCgoiU6ziMihWEasgIyZmVWVg1q245sj/EZe22vvvvS//BOvTuskBwGqix4UbknAvZg5gDeBGYjMZViwpR0DuXJNXO74DashigwBpHbYn/DnhSvB6uZzE98PohMgkcWerQAhF3NQVcaYKBFMQUik7B3TZIWPe0yzbhqfmhHnPAZtJeU/ikeaDCtowc8GSbpQoOGELu85ZXgIzHyAcNiIHFxtpNHfz45zTWkQz/ShAyoKz4BcoOxlI8/IBpc2XzQHWlQ8jvDbVAnEp2hNuXbdnWn8f7sN8cAy5gRboTCBDYIU0gUmcVKhSCyExHOQpz+NlSkPTLs+0lXKqXzf3KuU808x211U9Vg0Hsl3gBxNfW1yVg165hYs8lodRML4xgbqkm0X0uAhmH10NLa46epaSIIrVRcg/+DOf2gavSsBr/SerOyAaPNkqcivvvgoAqZFt5iunbtwG3a105QGtBZ21wWLkZSjNjQuUOjDZC1ptxUX0c3NCnA53zhybaJQZYseRyiRtdVQoRIcnl6dBnk5FopVBw6pGCjMK2UKMbJg1WkSFpaVU0UzHgtTJ9JNAQIl3OcLABGWO4+2Lvs6vP/305+E27PZ2u93fzscdAGICVAgX5T7bjrOnVosQn9x1M08tOVpD+0B6t6hDUI7RoQ3aEF3u8FSpG7Lsog0rw8uZOFQozjkfZ5p/unFmYsM555x+wDY7nhyJiGxeLp3wTH/qNLJNe0b3eVlr8hqZ1MzUm1pRgpllApH9XxmB9O32ZJ0lkVaOWFULzvA/pR1QYcdegbjYySUeDlz5xJOn+aOtP7Zve62/1Rq40H5l2c1sj+JbSUXqcN4WCSMftMz6WcbievbUbBCptM9w5Qcbp0iJIahtYW8g1eJK1yHMavIjiZmaBDGB9eQnapThbJoKKqrMm6O8iPwvEiZQ5ESuVPgxMNhjxDEhnDBE+gbKzFWJwGxArLExdwE10cAHFZoHjnncHnq7377cfv7TT+fj/dsvv/rwOZkiD3UGhNCMCR9JdkzjJuV5lyKiWD5ksj5t5HSu1NOeIbNUyKFJJrhoNE7I3DkOG0eKF4UMmnEVfgOaKUR0nu+P8x6aofJg7h0EsHMXZvIR3aNhndBgw/qvOIJZ4a7cr/CiHeDKws8q87qnClFFAOtT6STW9ksRltes4a5I2UKQViK7VQ6szqAoduqFBXL+AZekJen6e+/La/3jrrFScFVVtv5QmE0/qJ1Ffsya1fXNFYXHxvnBZutXuH7VN+lSALFaRvuPKX5pIGcIKRFKmGGmv8hQmS5pxrSWku+pDsxRNtYi1IoZ88R59/GFmhFTmDInAiZCiBChpynBUiQFyCEoBGeyE02RoIooWFYNQHNpUkYfx+02FTrv891+/tPX+f6v77/+MsYY5g9ajsDJE2qr5XbF9KsAWgrVyYoiiZGcfzdPSywZMGMaZeakDR/kIAv8oXlVUMnSSM3RPcja7TQAmorHPN/n+T6rJCAzc+NxmHvN/k34pGLzHmDTO8oVIpT7jwjJvRi8C8OpjsDy+n3DSECaKs/ai2Ill+hqeUF1jSxdkfz19SinuRrUsUv91PsMzEPM7EH4o9vGPANLEVqfFen+X67tRvpca0RocU6e+Xl7XW4huYUJtJFPSf18jlUs0gyON6eCLgnkfx3iGWGBRVGx1gSuKLGNJMzg7sZw44nMDKpXOHl+9UCvS1ispYiY+UYCMSd9Kh7IGkCc5CCEmkUs+IGI5JFsUHgXGxrsL/giMWWjQmFE8VeNZo6hmObuw8cxHsbjGOMYt7fbGIfil4gYZn47WEdnnsSbJTxRWREI0t2PYWae1n+4+XAfwgxNAG5G+jgO9zHGbYwDPuiH3240K5W9rJgCaI48leJzd+ihuM/zu+KhmEmilar27t76CSSJyg+256TteEFLuio4BcSFnsToc8YEq8JesQWw5ENWtLFXgzewrzZMVNfxh3pSRhIrJ7i6FCGlzjY73lm5wv/+o/PPsxK5/bQ27i9Z6Sk/ySD4fQ0aF+9mFXLRlo7FTEepa/Ui2QP8LjDI3WpsS8k5XA/YHoAkWAMgw0r0ly7coEjo/dCbIUw0kcqeVQhTkSWFnE0Q3bbWaQQ1Q06aJ0mRKY953sl38A08aaeCdLAxbcAl0rPC6NlDgJ5Yi+aOL3ZLDSCElT6qeY6vz/gdbhwj9/vL169/+tPPDMx4fP/2bbDVDWICSIIVEDS6e0aqY/ht3MbhADLyd7NxjDEOMwfCEXTz47Bx0A4bN/ohGz4O+ABdOd0sYTKCs7j8EJHa0OdjzrviAZ2aD0S2hnVhpG4DJdwEwCxnmkWD9bsxTThIUKy0Cxvc10lAe9MmiW0JoTrI8GxJuW6Ylvhf/Wi1kWIFYH8xh+fMGassoXZT1hJITQfaKEqfY70ygP9ofVrBjHEZXwB9ImIT2yLZ8s8rtlqPMTtcjf4X2Ay9trhj3XmNGEk4VUNlCw2Y8zR6bdkq6IbBnWCAMncfnDNITyQaQih6uGv6HUs+vZGVYESYkUDMk3zAHjbeIoI8IWNSgDQlCIZqEg1BooemJsmh1emaInKgalgNzCzKeUkxaZb+ADbG7W3cbuM2xu0w4zE8xoCmOSDljEWjQiB5ux3HcKON4WPY29sxjuHuw48U4nHj7XYTCYYRyTiSDR43u71hHPAhs7T+Zl7eHUUZjRkqhZ/QnOd8zPMR81SEZmimc0tp1SLYkCmaFAsKo6MhmVbvKM+Ymh95FZJTehWQ8m1mVbmdM6TZCWW9J1sHUh4jy1ELFVxbQDvi9eIu/b/HG6n5s71Up6Kq0NW28Fm0X1Lr5jUa7DfXmgPz2cJ/PDmAjgzUE5pWlJT2DhtBaKXh5BrAXf7Aislz5fJSDQm5PEGRtrHgW5ZSGIScQgM18yR5H2P4HAHpUSxGW+AyYMUAWtsjAAR6fAmICM0TYOBO3gPfOUB7M0zIPGftZmxbdcMkEE6BikxVPHfZKn1Jr2gCOUCGwjQDdJjJnBw+buP2ZmOM43a7Hea4Ha7TFTTDnA8SVIxhY7gPv40xjjHGOIaNYxy3cRzj7e2r0TVFtcReINVuBMhot8Pfbnw7NJzj8OPGkYJoC4zLmkl2LqQA9HnmSMjzcZ7vwqSDoeFepVIy1SAanEkfCTTURgJdCs57wACkGhEAmnuxgBqy5+IFpfdhKRql3X9iBzQWtLbADZncwosE/4itAtyVCDJTlz1AqZ2O6BEX+iPTgLaVci+pC7Ty9ZczWCu9Y6bZn20o2FiBVNr9KFr74vlsOTKbZNkVPVxY6lMTk1SRWlbsdkjXaFfYtRLxejx9jKsNDUKy4I0pn8NkaT7u5+12vH/XrPahFLO7eCZCZHVBM1uNwlIEtEzDULxTw+CUIRwwwSBn7QoRSUjNfuMUG0pifCnSQMpBaWTL2FmeE1NN5nLzEe402nAfY4zxdhuPYzCOOacZk6PoBkK3t+M4hrv7GG9fbrfDx218/enrcRyCKeTp0+pkYsbMqkCQty9v/vbFjsNvh48Bc7qnjpoV1kQgFDMDazKmTjFnJNxjvs84oyUc8jALcKliKVkTFwKAVWOEVkdYXssdXYhZbcD7rTKnEo7b8fcV2q9bDt3Hu997q5c5fy0zv+UDm7qcIpQqsdedjfW5jEf27fzx165y/KoE/OZy98/pEUc2N60IrI042vironVWL2k+cH6pPmjnXeRi9xCktNzaMitaq+78/tJlC2JF8URnD5Y8fXPnGIjQ7W0QjAOKVAXtymP1/KB0LAhadRhIM5ANRDSFdHLeacNg4iA8JfMBzUg5nLwVGMl4Cdmo0kL11md9+KqTB8oWJlPVZCYjSD98HON4GzZow96+3sx1ng+nZYOwu7mP4T6GHbfjy09f3r7cjPTb4cdwNyCFKixLpTOhfMiMNPfjbdwOHM7b27h98XGEVS3d2hmnEFA230JTMde/53mHJmLGnKzIWjFDR/bkHVXzz/PLkv5r072ue3Wr5dXPO2czMivNWxFFXfDQNF4w0ZoI9kN4ActmkciCBKVIEvACiBbdJfOerEGx5EGwdmzZvigd2U/hABay8QmLnH/Jyqj/OI77/f5778vfew0mTbBVCrr4tp6NovzlXyPHmLj3m7Uii6uAXPaxQJz6HnXZLmp6ZMrus73Lsg2FM/RHW71SZjYOi4jT7cEAgiZDmngkbL1CvNQZBaipGScOiywr0KBgPExD8R40zIM8ME/zTE0IpNZQIk/cwTFks1jzUXJ3A7MPr86EWONY6Eazcdhx89vNv359Qzxm3B083Ifbcfjtdru9fRnuNNzejnE7bl/e3D31fMxdYo4FS3jKlVMQggbBOG52O+Q23r768UY/aC6r8SfSzPICOiebMaEwSfNxv/8657t0SjMROPOle1Rt2xFBDhJCAK6cGCEt4aVsuDVLzTlleX4h+89XdpFz6vwxEb/KBLOzAZliLQ2DjhFgNQau5hVt3jfvk7xMnYNCnavmu54qB3NOWhEW/iZP0T/4GmPMOevAyff399vt9ng8juP4vXftH2LlRLD39/ffe0d+hzVUSGjBuOuJ3THZ7J2xrQlgj+Wewyj1wymwdOXy12R9pBImgD1EzDekGt3KBhLPKfUCKeuLx+HzRMxQzAgkwqyq93KVHnKk8PCmEJ4n3IYPaVpMxKm4Y7o4aCf0QDDogKNYT2s6gkh5TaNdliejVlppJqcyNRYvXXmYKSNhTJbPuB3Hbbx/oxltjOH+dhu32zFut7e329vbbRwDbre3t+N2cDjdfRyCAU7L+nKG/vLrcElzG4PHsLcvPN4wbigGfvUWkAgFgYgZMZU1jRyoHop5zvkQTqj4Tu5+VY6VnwJw5LAZCyiVk6pUYLv/XuYY7bnz14hpNbEGaNInisWTSH7h/pmm7KXjrexR4D76yOoWarmRNPEEWENqipu02KIdXHQnmOL51v3Drj3qJ3m73eacn9z6f0As0MoZv98e/T5rrHweO2LbGrtWnTV+KRd8WPtDDzQf42oP6/LdYoJWKlCVOuTU25UrEJdekFaVIBWACdIxBufwc4Q7I5H4Fe2V6U0MooZi1+5I8zxpRg6DFDNwpx/QiTjpQzGz3yyRAUJUwEh4xMRqP7XBPu5KcWLhV2DqixnMbF40FYiwYX4MG34cQ6Gfvnx9eztut2GH397e3t5uhQEdhx3DhiWtc4Luo6Uu69jSvAVUGkM+wmzakB82Bn0glXkkLFueghY5KqZawTKV0Zzvcz4i7lRImDM7z2pGdjqAOW1O8wGQCsBZjXvpF1VyENtAm4W3pKZIIojimvCYPiMzp1jv7J6SvqGuppNy9Gw6ct2iQI2vaebCJUa96hPRhWhr079uzk8I+65y+g6IfcK1ToKkzACyDvzZfEBpAbFsdhNpLnMDCYow834SkUHcnBNb1TdVJFHutKToVeM4OnzrUoKaQQSsPH3jCG2JBWk5ADY1RoOyATpA+YCuEkN+iWXlOE0cFnIVkiaNPqZiak7QaIF4UA9oKKY4aQdanw0lh8aYIZ2inJb6laxicfJAQwo0XYVTEmOmrBzFVJkzcxtj3G7jy9sx+NPwcbu9fflyO96O4+0Yxw0E3f325sdhzQqCH7RLjaexb1klRhmAp3600Q76YX6IDnpiYuluk8YfmCV+F5UBJAd0zlNxEjBnIfVNCRBTCJrnGWNAwXnKvOhZU8qeiSi8L6oqv/n+vs0oRfZlr9frvFWdWRHTa/ZXEYFSnBRFvgKURZ2CCBUyt+Us+rapaRbPd1EHN7TVpM5686fIAPb1aTuePqzzPNPu7yXMz2b9kQ5g5doAL+xUVKbVC03ovl+0Bc/yX3ZZdUxxaYeByAAy4zsU6rO2X7YmQ9WILKpygQBmLhGyBkAwBSFE+MDtzSXFWT48ZhpuB7I/IE1fMx9MhVVrKmacD5Cgy07Mh3GQt5xADE9ZUEAMQOeE8ZwTBlMOFQjAQgFxBoAJyHJaGPvQkX0AWU42G2bHsMPscD/8dozh43b7cvvydns7kiNEd47BMejDb29j3GhD5jTLNrA6XVE0IEtBDCM4RKcZ3c2G0WWH6AQJy+uVhrzaF4gaBYwZ8644I6YUZhmAl0EMRBTApQjExJxxnieIlgX1dPo1d6yie6ByQm0uvN4PIMPz5H1mLWEFpOneyBUY5M6wGEB5R2XMzipoZ69BVhJ2VkLfb087sGWWdcunMut/z2P1j7uSEZQlgc+cAZjZeZ4k150zxjCzz1YHHl31i2Zm8JJ3vyihwOYnmrhdwbKxppGUKegnuGgnCSJVUMb1NLYKWO2HrZF9bRSSe47abLqiIGEmHzzEOT0mcbfzRI4khHSNJwSUwSnBkFsCzIzz7jRMgm4epRRt0UKkbNchZsk3Zg0F06mAaDIWJpG4RReKlUG/wdynTB1efvnyNb5/m+7jNn7+l58dJD0dwDg8GU5wt+Nmx2HjZsfhfjM/4C4aYDQr+aDIOmhEBCkbOcKeoMG8MJRM1BIXgwSGpqY6JjaVDI7caYzWz8u2ifT/gBCRp0YzZoTN6RGs+Tc5JIee7XJcTRgCquRgH4zvNpFx3UpYxp4o+u/CAEtur1K7raPZrrsjkDOBEoB6Kl81KJU3F7dIn5v1/4xr2X13P8/z02oELYX2z9wFhqSB9gNStBeadSMO2zFUElCPngktXQwsu43EJLDXEvZgX8rmoMSIuEg0ALokLM3lJCp5TytQtWWZ522ayB1iIM7VapQDbkMJxNPErvUxQX9BEzNEM8B8KB7UKc2Yd/CW6pgoyRgKMPOMgUkqToEJ85Tlkqw6h5UFzPYeqw+WoEk0c7q/ff2Jt5um3I8xDj/G7e2WVps+6MOOw25vNg7azXzAXfRIulFkMhZUZFM02aoZyGzGaKZskKJABTO3TR/YaF5WKHjNv/bhmg6cCxxJR2MqnozAc55j+pywiVQYstDqMAgE6W3EsSq4bL1oVEN2i8XmaMsqM+TlmwkKLsew2e68yxL7GmWsYIHIs9LeuBX2tGhBABChvQ6836ZdoPl0S9JxHKmcuD+qn2ol4v9JaGD/yRrXjwRSWEdpxfPhcdQ0EpNKuA2AoEhteGvgAwhETopX67Dn66V1D2Cb7bdn34UD4JoeQzL5lm6WCUdW+8hUN5DE481ixnlOm64pfOCKAABSMQ2h0JRMqS8R5GM64TTaIYzQQTvBkxoMOlzGAKPqjUyQCggwEJAZVCygND4L1yZCmGB3KFTeM8xu4+1ImweauYOUDxtDNPNBHzyGHW/0ATvoN7iRXp2wddoCCgu1XVfqdOQmq2FAzLkxoMFsVp4lSVmcUfaH1WRhr7p3FVKivIfYyA/OU0TELftpGUFlYlWmPgWXAKmnLEqIkoXojNCMEci6EntqmCJYyE822eXNhi0DWHkU1x24koLFA+rb94L7d8tek+SqQIUGijqF/WSLW2s0OqH/hJTQbnH/dJ7vwxqelTRZDXnvPBzZqd89QH27WEMfF3EoU6mUnM3HOsXA8gt2nEfNRMYGDesSkCZ9JGSdpA4Ku4vOqoCoal8+EG+YkRPZ/Tw1z1IHqCAQJSiW1ntGwOgjDxaYd5wWoIIYN7NJCnMqQXzNPAsijRkozVRIojlDzAkBQHUubVzDVENL3QaRNB+3t5/+5V8xzys6zrPmDnMfB33QnO6wETbMRnDkLDYb5dikSPUhIlI3IzSrc4rwHplmtCL5uG3BsFJyKYfpZl3HzU/zOCNPcoXSmTzVxYrznO7wYeec3nr9cZ3k5O0aEK25ARRlikZGpBNN95n3QYYXE5jr1ihMpm+ZDiEIMPm4eT88tVugEMgWnRawatAlDvjcUbwyDPWti0/4+K+OucV4IZnl0E+12CKDn7Dwu69R4vusQaoAesJKa/5cTG0uCjXZzZ/C1DSCVoLGS2qxVxIBLeYEtskwnQFIq4ksPyghEqrGU4JWesykaHCHYo5j3rKnkzGncudIS2JKCEr+u5LXSQI6p/hwd8WJeTcbmnfYqTmHY54hRuBBG7SU/7fio8KAkCalPEmCZ8ycBhdFiRQzvjDOTCDM7DjMYDFjzsJGIHcHEws55MPc6QPjoB/GAXMzT5WJVFJCaWxHdjj3bizUxQBfZNryTOjO2HxfhBtlKHkNMwrDXdPn+VD0GK8m+5Oj/SdmRNbhHdk4PAn3AuULB8NFJFtmOjZmJxdnmGSoFHny40ROviyPgDoikGaWN4JYORnSOUS7N9akhgKY6q+tNsqtGlBffY0c+HweAECHYlbp9ZNmxic5J+uQP60OaK7RVOtYFJ3ugcoIHbtJbySCiskqEiTmjW77QukAr3Jf6v/kYtqJiAin57eYeZJK+0PswQDC4m5ndNfIQZeCFYohBE4F40ZJMf3xmDMYkfGizoRpBFFmNJcQMR+YYRLDeBwR3yzeHvNb4IYRtEGHiW5W9UxzIUSjwngA0Dwjk6UsinabE7pwUZUOM8o4nG7Q6TMUpFnr/RPmMs8agI+bfMAH6T0gxWZOPSwFTlKW/NNSO2jMBDSjZ/9Cm0OZWTJ5RFFhSYpnz6A0A22MYz58qg5EaG2PnpkeETFxnvM8w51hMHKekyxlzYoV8rNF3cmJYnnCdl2HttJKOlXBWoC5l/vPEQkLISx2f9+CCWZFdOqzkJymHoU0Yy4PVBUDY2NRyXFYoNCnMHb/0do1gnKtuPj32qW/5yL5eDw+Yfazr6ES7bXFowA+3gR7jNCPccHKYFI4ijCU0vy61KR7O43LdwW4o+qqDxdWkx8ppLgsEiq2S3QqP2Eg6DTgyBA8QjFxRsz5iDCFPx4xFTMmaDPOGVNINWnnpPs9QRObw+I+/RseQ67gmzhs3Mbbl+MYcnN5Ug8LtVbMebIHraR83vJwia5aIwwp9iYZMChBmTVlLZRJnaQNG0co6fyDNmAj1eSyWgpa0lpDonmdq53Czmp7yDJAInYrmWKJboI0xZwR6FwvpzwK7VqyfoMIiZLOAOFhrix1DAkRnFHieeUkrpFtuReB5F5lX3eF8R+LrVzMAqaWa0GILP5YEj9XurkQmws8rPigRwFHsxFyyyvkX3cxy0Osb+9Rz594/aZG0JzzM9jEgjE+/cjMoS3124D79fzsL3aEu5DlTODdOkjlKtotJV516Xh/1i6L2TzRYgJebyrknu4kF8JeKAFBJ2gUPBQKPyYfJ2vIob3f43HqMeOc8TjfZwIRDOAxxjA/j5vRzcM8JuM8+S0MwUfYV9oX8znO0E9fY/jtMCDnoEMmEJqBHB2fbWfl/0RBEVNTCkOAMjP6EKDzRMwUzkkPmlMwBcBH0EEnXXDSYUb6LEXqSsVSptSIVXHFKo+2L57IUS1oymdNuCzdHtXY5QTzjIys6nrW+N0A6ZTCjTOCkLkAa7yk3NsM80BETmUo4VCpav4GD80W40kfHu2/rztKap9Rr6oPEytoyH/UZn1Zf21NBkCzjhPu03SWwnn72trz5Vr65rSI+LRk0FwfNILU8NrvvV9/j9Uzjuyz1wASMFiVsVz9vJVZb/g/S49Ilmjh11a0mxXSp0rw5lepy1RVIW7lE2nNc6I8KOvqImkp91joAYDLCuRXWEjm5giBc8pHHDebD+jbY05/nPHt/fF+P++PNFSIOEGZ2ziO2xxmBsOXL2887mEKzvCAi4bjMB23mOckwuawgSyRZO8VQJgiyiabEUSJE5HJaemDdBshgYGWR6sDARNWgpno5ACdPmgmePpQWM2KzOFq5lak+zWWsRC3GmrWpNnm5/QpD1VnMjL5MgsitafLQcDcPKfxWhVV81+fp4bnMIX5sE7F6OZv6QASySOL6Klu+EpYjERkZggKhb20NV7VgurXrcovgdJ3uNzAHrmzJUm5veKE6Oz5QzUAAFE1Zyo70ci9HP3JE4CPzPcxxuPx2PC6P349gGTWAPKmOo7j8Xj83jv1d10jLXxc0b3WfPaEX8n1etNAkzVYVVtbc+BTBW3Jgl44UmXw1/10/WBYLM8V/fWMeFvByAob24l4FwPoTkk+5UccYec9x5Hrcc5//+Xb98f5y7fH+0NzztvNzSnGly9fju/DzI4bH4/vfiOOhx23jOnhmOY3fEGcEKUpPIzlKVnTDpMnkyVtn1zlX0qRY801gxFnTEp0z2HyBXklZCbQXHDQRYcfMIMZ+gxXDJ3oh+dIxyRnldXjNQUlARmtGVu8bFw3+EZknxiqUQDudhxv835UB1gsx6/y1CHN9HScUzyne6GFSQSKkBxZbTGi9fhSvaMuqRB5mwEWOjPT28ZOsEEboLrAqk6b98gCaqSo1oq+f1Zy8Bv/Fq7YVXGxVUyuPuGuLrxWLXV/QP66Tubvu1d/h3W/32+3G4DPZv1RaqBKfkUNnEo6dv55PS3LygCLslmEYtsQ3mxE+hGHXQ0Ee/IOFN0vs4nq5u2Q3zy1FAqsaF9SibxxZGVvBszgA8fNNGED4yANc85ffn38zz9/+37iPO39PqH7uJEm9+9vb7effv75p59u5/xmjzi+DI/78ZbkU3O+UXcEdEqe6FQOArMOWGcOcUTi482FlZDBpyElFLIXLcUtViEhbTrLhNNS22gq7byJgC+55esBtC4AJIto/WGHti95AyIUHfVn55UpRLG6CEJGGz5ow4Zr4owZCkY4KQYRshigRGEIWehGKgnN8xx2IFuxQjSEYEUSLuRK1YxsK4tBThwSqlak9ln0FWe0mggyWxIQgRo43GdfjYBdqUSd/4o8jBZdjO+ARp1SLE4ztlTps68a4LHRQ/Fp6gHHcXwSV/fjSkp9tKwvtdmdjrvrrfsJYnHpMhxuhJdVrgMRFa9WlNfFums433pyLyLHgq1JXsYOce2TkT5jGq7xT8UKPXjA5wnz8MPpmAB4m3O+v8e3b/PP/9/927dfb19s3PD25m9f4v1u377reLvf3t7eHre3n25mNoYTRFg8KH6lfdHEPB0CbWTDKkxzShDNVZLUzTEBNOcocY2ZNeNEZyZmxewRyAn3IAI0Og0cSnk1ALQSnBZQaDqVtM7K1Vhk+sZ8VITVJLRlFhLm3udfK8nLbizaMBukk56VgVnCHcj5l6WploTPoM0wMlwRkVr67pZy+q6sgqxUDygVjArtUcH7VNt3ksAsN9WKP2nicyBwQT+FalX9AdVOnL9pnXIuQk9KV/UdyLq7siEg8ibcE4hPgHD81xa7R2z5yE9SD8AHdPEzrdH8nCeChJoLlLF/9Gz3ouxZUs7TlGM9iZ16I0PP1VaWyPKcV2oJJElmqx9UBSGtPBclvDezPkozV/KEIDB8kOEISPN40+0Lj8cYN95uuN3ojvl4//5tzjhoP52Tj2+P79+n/u3Xf/0/7Kef4+3L+eXrfDsfP83TaOaQ7uYn5ozzHn6GxXlnePhRUTuCMy5nl4XRjL+dWQzI8D8c0JyLBKsqbIvEjAAt6Z5wR+L9FdmTNJVGByQlxB/SmcKo1oL3ilS67gKJpfR/cn/UbhzZ/tM3tplJY9KOcfB23I/j9CPGLe6n4jS3lAKhGXQKmjPsDCdnwKZ8UqMIOgAjLruMhfix0SSlVOCESrzTipRp1dZRaQIBmHlNEk4xJyZXlGbpLS02e83ixV4DjABkr0gmW/XCFVsUMeGZ+PjyALU+8EEzD1g80RqE98d1mGpS0GerCY8cr64qAu/8inxhg3cS8FmSvLW4CHZVa7z+Up9aBv1yJAnl1Kf7G4X9gVwP9bWtFpRIihAApFRRwuYRfujti9/f7cuX8eULv3zB1zdzx/f7L4EBegROOfB4nI/A/dfv57/8K77fz9u7v9/fQ/NxfvvX/+NPwp18SO/GGcBjwsY85wOeVtVBNxsxq+rb2nGI7NmKmHNSyoJ5KldWPEygpYqQ0tL0FNKh5SsGMKet7wXQBOBSmQPtVtPNrvp9dMxcxndBIhUxF9ZHM5OdtJCRPsZwHxJJZ5aC5wQClpBXOf45cZ5h5pJLmnOeJ4ePdWuQ1QC8WsqRUhw5v14MBQ2hiXB2pQdFTarLvYEzVpN2Ln9iG9Gz7p2npADFUjAwFfOqeRgBCvyNfp8/sEX7K9di8aUPWOLJf9SVd8Jns/4ARkX62k19rhZ5vFh3H0IALYnHRJAAeD1yWTm81H1zsNeWZzUVo7/3A76UBdYVxDUitV6OEhbLOmHdqRoD8WZfvo6v7/av//o24/j2/fu/vPOX7/PXb3HnPCMQNDsOPx9xj3vg37+/Pfy48/F4mNnj/m7kl5/OOOeXr2JYnBqH5jl5HBwj0jCZu4eZs7oosn1WmpjQMAqnpIpcwVCY6OYd2XrVz+Gg09zMacmBskyYIlKimY2YsXiv2/kHiqe1X7vQJMwEk2MJl7ZTByKNtZtPpIiRF+gEYwn8FzfXPXmvfZXhRkMElN100dg7lfykBeo/VVxrklBEANEdJ2mOTSppkEYbJIVglURy+U3usUVuzdgHWN8W6yM1QbKzLnac0Q7mSRPitX5cWQpehYHfe3f+21e6uk94P4x+fq6HJ89CREVU+MFAV2pQrxdAUW28USM/zFe4DkBCmLfYcFQ2oOKt9zNbpYVYs/r270QFie1vWBrBuS9FGRq8yfXV5mkhhfz9/cuMMSf//Zu+v5+/vn9/zBPUeZ7gAM9z3sfpAM/HHfrlX/71zfjnx/2hP0ET5/28vT2OL6K/2/kmf5PdAg6b44jhNswCZBrywZygxaxqNEzPKs96li5Ay/Zn0s1vNm5WKkANwjUMnqt/rqJAVzKhUIMgV3Z1gf511vNSzrbgyT+KNLvuY5rRLbEoPZoXWS5NABSYM2oWTW4j6UwwwGbIgtEaEglrAUstqmL5nKAJ5qD5C5q36kau+H2/4g1e1QhpNT6bR6cqvMyiXanmjlLdFAeZWcyWM8lbqDYNFLno0/NA/+O18JAxRlKD/tgQED5rT8AAshXzeqlxUgJ7zLU6uZ7T8NIT7/isS3dcikIJSPPSDKBbsgaLsLgcQPf057KSviliJRcLqAoHKShddEPkTHG60ykLMSAS5zT6F/Pj//mf89/+/Kts/uxfZsxznmfcoXNOT2UBAf/fv/0CTYMe9zsUZjkQa4LguMfjBv9J9jXskGPouI2hMY6lq2kWUz48x3XZcmsiApZj1CKdFc3MfNg4zJ3DEw7KvuKmMBb0sTj1INgVZxS6nZUHK1nNiIWXAAhNzWwDy0JLFAQkKPfQUmHfh9+OcZvj/njc6xqhxh5nnWaGQMx2zucUp2LgnNMcPlkNDV0NR3m+ZHVZsgHMPfVeIVtRu5kh5QPbwWORegwRqLk3H9PTVa8TS1upE5DMxnj5oSVDXVgleve6pvVaP65E+dCoSOZnmQqoNdSSQf/HcAx67ob7PGuorS63a1lXlMX821DXzNNjgbbMcVjCJeDD5EF2AbNnhKmHdzMDOnfUGLFl3NW5xXpQtZ78vDY1fxCWMEVWkqUAwmgQ4dQRty/4iRRPuPkh8zf3OY54e8fjnI8T50SI0jHPW8Q85+P9+4Piv/2/v87z/B//159m/Nv98f5//o//EZhC8Phqx8+cCsPpc4Z5nKEvpAs4SAYBOkd2tMV5Dpr7oseIkW4qKS+AGX3Q3YYj0f8yfIsotRKBLokX4J9qfSW5kFOVVd0CkoKg+5EiPGD5oXkGGvWorRpgidG5j8P9cB8RHnGypI0EVONZPhtZ7z1ngBjhOdFnzpikyGG2goYq9rT0N1tQsEWhRVIprVQVimhsJm+bAE2zXlW37P74cGYk0HAlk5LW9NGk3naNWbiK1V3Q+tGvvNaPS90f0FdHQI1gW0HOP/v6YxzF/8Ya1sY3f78Q5864SyuYhuJTY3XQrLNmZp2F57iUpCxeAHEhuXv5Ts0Y38AfbmqRT74BH15MR4LA2qCh4kzZiAOUGczGDeYYfnz98qef/9/j//6fup/n9zvnHFPxfr/Ph88Z9wdjnoTu9/N+j19+uYs3/nIf45cvMd/m6XHn+RBP3uLuX6YP+phBwW8Hg8bU/bGcNoAxzCC3JXCPgrAizK3nARjNMmTPpq/YRKWxVBDKN1+detmMltLQqKHnmXip4btk8kUj72n6QwonVJmCgKmK0c2GRczkfaZEKGnznMSA0y27JS2VhOY550jRI5EusTT3DUDNZlvBFJML1O1dC3evNwRWMWdda0lZVuhTgS52rDckRHR9RYXzVjnIqgfkBytqyKFvFZykAMnLAfwvFp/nB6AFJJaU9O+6d3+zlcPRPkO148MaeQHj2dq2UIZ3pdGQCG+C/oVVs826N886cZ6S4X32AWpiBgu4h6AiG6zhDGrDscca2JgwBRV1/a6ef5hU0whoMHCAINzd/cwpWm7x9vbl69fHn3/59c+/nr/8+kiQazrnHGQIX+aMiGm0798ekiKOOP/9Xx4xf5Y/HnY8fIQUc/zpYW8cX4Ch7+9nEHQgMAzVR4akcZIsnlLyIQVnqfar8OpLQpVmEZoxM9pdvW/rhBQnK21/lOaa1qkEkN1qShtfT2yjSXm2ZsRk8novzI5ZtU/ACQmjKKdvDgAxlTs5z9BA6V9HCgNBXtp96a4is0JLRi8BXeLLeqJgCkKOgFaWjpljbCKEKxNXxw3qvuuy6sv0l8/TxTLoilGEQJgQYhafkyMbqO0sH/Na/+Ha5wfsEWHqxy0g6J96Lej/U0JAKgYstyPPwLCeESDHcNN6sl9JwBdci6vPq4x5KkwVQN+9ntnIKsVMgEhcUlz7Sc/gPsUlVsh/eYiaLFjGBZ2BslvDFILoRnIc5kYZNQxvb/z3X8LH8fb1y9sv/PrVf/12d5vnA/eHzNzH7fHQ2zGE8zEf54n3bzEw7t8Bnm9hNt/1xcdwnnQLCUF/iKQNz30QNA6zFKoLWABOLyTNZIEZE4bFXQmJyokFkYLIzbmoY5/RPq80IdSd2/XjjBBWFbQRozrtAILMsb0s2ykpgoDmzHpwYUkzhtucKCp+gSRKBbqkueaIeONhVqODlYfA5NhgRnjVdFFDy6SC+Evcv66xNEvpArXfy+OrTXvlTUilo/RocnPVrbaTytBzES4JjDoRq9hQGcOMyBp5BRV/syfpD71WSWB/xd2XP/in9gS/1SDyWVapge7gDK5wKx9erXRP6LF/TRJh67CjBg6uAYZLEXolie02qt8V+BHVXXhRQhPIocRXuLf+zeooGy9amPX6Modguh0EZHaaPwKRkHENYLRxO3i/x/f383zYffjjhscj7o/3FEoGABznpD1ICwc47n7+6jYPh8A4zSid8Xg/TafFW450Hz6kFMdJQlBSqiQJBUqnaQpFTJ052ytZszFPMCvcKho7maPFchsNaTeKvRhZG6FW2OcEJOs2AJkhZmR/shQloh0z5pkjPnNTJEKR0yFJwl0RJdcXmhE1CAbM6ZFFGBVarucJrKtCTxZpUPWgxP278bszwkYi10u5Ihvr0ik0nNWoUUJOeZXNlGw2rHE4pDlY3Rol55QVqd1dvNZ/eSVgYmY7cvJPOlqSRVa7Ct2fZw0AviZOrdka2a6qQEfiv/nhzs0lZRNtoq35/tU6yAUH1Zdk8ZaXFVt0gn7b5o02UBxPAFHZjK4tkNWHXHEuOHPvboPGMJ4xZYS7j6GbcxjfjvnNT5M0xv3Qferb9wdt2Kkkao7hGZySMsawcNwdYQHYnOdpuA+8uRxxaHyN8VX6QtzMDPJofj0kIMgKo0MEJ5HUmhO0nLIYISjoC55RTniMOBM8S/PnNlCqlio/WJybpbERfc5TBKKsd1bLCwiK8hYzzogTRFL9pICZ0WKeFYwrlfpTZo7neR5+ACQs5pRqeEvzUIvOv65R3ye4fu1X+8W+fhU3ZCtwfXk2Z7MGuSSkljdqdDPBhQqFkLkU2rxLAluVpOYkdOahHhn9Wv/1ldBudgtnj1hOmf+99+t/Z+Wta2Yf5FE/wxq2YIHWZ69Yq2qPC2nFni9vhLAM5aiUyaxxSzlozTckd9E9vYNN7Og/skrZEd4KcnkJyal0IlYQ3BlAPd5VW2BqKEiSJpRGX5TjZ3PXlzcf9jioYf7rN9wO+3KMxwPv9/P7I9zsp3i7n+OcYeTbcBoccsbNeXC+kYMyMs6ATcPdMBg+5jHi7vOO+Dbnm2xw3GIMsyF4Giy1ABrNyAGcmllRNwuALHh9ZsdXgkyOgiwMhpY/SF7/JdS6neSybW3kMp0wqWhVZgxVKB1zthQrugK/VVYaoDPPC4cZc0QODmDNRshxciEZcqTXqkoo5+Fsgv5l0GkQyASU0YO9CvxJEeremZzyMhenoA6QDVH2fuYJWGfhnLMhnuWEIofZ1W5k2zN7quRr/dfX0oxTrwUe/N679l9eueeJYn22VoCRFjbhnEtaAFBqMLJEOtOIZOvv0g0DgEvNLdoqXSBN2/FYD2+9uoYxVSk0t8Tq6a26KJTov0E9Raxh7jR0ioV+lCLmykmufAKQ02xkmWI+/Px5fnejDx7H8faOb8d8f4+3G7+ecT/9fs5zHnOSNBrMwg/c3my43pw3p0mOST5IQpPTEbA4ON/9+JPhy4xvHG+KN8NXWET6KpYvHTZAgBHl7EadvehguLgxrJysMErUxC5RGfw3X0uKtIE1uazAn8ZMqvKZ4f+kyURzO88SnzYzUnttPzDZoHm2AwiiuTV4IjCoqXCYhHOe2dqWqiIi2F1tq5S98rZ0RdkVkfufw3vVvYSxcZEXEolq3VrgECt6SFyHMPMsWeRJ7PpTI//pkOoIM5txIFo+77X+y+t+v+cPHyz+BzHRf5aRWwu0/L135O+9St9DlWXrguZZcH5G5XvkxZJrr+e5k/0A4N5UvyLqLdMMLJS3igcJaFzWYXkO1AbVFqmElJeFWph3csBV/O4u/zXQtILgJORwBCzsfAfv5jbG7Rg2TMP97Y3nw89z3s/z/YGQhUZMCGEmN7nBXDdiaDrMdA432B08IQOI8zswiAf4hTqEL3O+Yd79+ELCDHQGBXowZ74f1TglQGbkrEmNrMIlAVFVyDRzT4+3Ou2e8yelf8vQPqPqPulCTrZXQGEJRxmH8cy6csxz5uBkWKcYqQjX1XtL9dIs+mVlIBQ5+Hee0zw7tlhTzgixIu6l/pQNX22f2XdUAXh1k7SWlOqvlwBZ30gpIwGUL0xvghRTqgZg2gJxK9hXfoCpU9rJgT7nA//fvfKKr+v+T2H90TXtz1YAwHIAgCImyKyeCTNLdkqx+6ucKyAIc7N+ltn+givNT0ZfJgS6HvW9uLeBF+tGKcx3f7YrfBBUmHbvbWcBFC4Rj9oO1/6soeNBBnCaPcYIgG52DB+pz/DLwz0eNueII/Rl8pwx5+M8K18xYhjJOExOGERN0wPiqPHlSZpyxonzO3RAX8Sv5/nd9HPpJYMiwAG+yd4MUhg5jIj6lhTCvE4PBIURTCrl/ihl11v6OLGYQ16jYipvUOkvwamQ3GRtbfOnM5GzzsZSFUIi6K1KCjOMQfOEczJ67rNdiUyUGy4cRqu3I98WESPnMEjZsbsbhScj3I2H6d2yhtGX9Tp8ds9wZ5pAl5p7qlChgknMCsmyFaF0hxYu9LL+f/uVZvR2u61egX+K9WlDgRFVDMy+GGs8FspxhBEGipoxs0jQ6gQVoAFXvQ5YKEy16eMa253ZewoBtX3PVP1HMrZg9Hw56afWrNPkBSVEZaksnzjwJgq2bU1SgCImBJpcJJyDw0bq77jB6d++x7vhPHVGzKn745QzhknDzc2NksHFaSApM5InSUcYEJoB0AZ0WjyMN5uTdgrHfL/7ccBZQvd2gyUhJWiDkqxqmGwhpZByWgsAwEhjaZ4qsX0zS5PHireVZXxIaGmmSOSJqR+XVH/QZJqBYAI1ZeJ5HCMmSLmLdKTkBLKXjuZ0p5sNT9IH3W0VY8cYPtITpQMT7Lq+P8SAi1CwQPxVEq7McivvpwsvGmsE2NPhUWNB0fWnSvxSuIJkpMRtbUaxyuMEWt5cwvO+vdbfYKXRXwShvBPO8/wHZwetbPWzeYKx2HhmlhKdqthSkGV3kooTpLyoWWvNSsBThyr17ErLgm+3QjSw27XlqDqumZW4WzP9U/f/agcVjVZCygqAIfHivVyQD5sx0nuSmHoPHjYbdJkXapIFQfdj6P093u8hNyM0FROhKJkGlJpB0noAEWFZlyhvBOlMFCNJMdCDHGZ3zCE4cSD7wLxOsUJOUlSkfj0tNS+nLNMYBcPNnFlrt+yoME/Xy2wOCAMC3e4b0kJdkLuBol1ixpzAQ/MxH+/n4z3iPM/Hed6BU7rTzjwbNU4LSoU6T802ow93N3OY07M7AEaDezY8dwK2IQA9Qa1IAwX7m8VMs543ycrhKj/ZAogkwWYwn+Nitit9RSE0egDQJJgCVV27WuVwGFNv9cIS+eoE/u9ZC0vJh/PxeOS04X8cTell7utGbV/1e+/X33uNFapXBtAwdMdHxQpFIaoSwuo37UAqS5OZEWdLBv24NnmA9hULKOjYDqzn/fpYI0Lcv6shh+stQILdyIpqSfHU+F2UGyBoVNAHoYjbJEWDuZk5OSNo8GmUWZTTEsHqjc1TZMt6SD0UUzGFdmia1IM+oIfppjkQZ/Bwr7hcabfddGZtWwANrinOuaoA4Ax6AS/mZm7uE6koNwAyC8bXpPNMiGBEDiPrQDg0HzHvMx5x3uf3+3x8P9/f7+/vj/v3Ob8DD7CCetLcqx5kXllCx/5wgyFKJKpmuOsa4ygtfKbj6+wybOsPQqv/eQPuUDkMgFZz26o+z9F6/1iD0yonyDQJOWbN6no3Julc0+oQrRD3+Z73v/fKmnDOXMQ/Ur8YS3hYktzd3T+rFAQvuAYo40taMvqxHmY0spPyjBnkNsij7szsH56qu7nlLCfkj2nK19CJeoI3dDYiQpETUoo3zyKX5ExEbqz/PXFbmiXs/oPAiSRRqvABVMEzwmIcmfQAovthpvt9Aozvkw4v02OERzDLzkpaDqAQIrJvgTUjDdCpEGBTHJZyOxQkI93FB40MZ2r2R9Vko3KjEwIjMCsQjq5x2hj0gXHk3tJMmAnMWw0eqFaAgJKTa1DEA5Xezsf9O+LU+f54/36+v8f7+zx/vX//83n+EvEOnE4F5W5ZYBgwmoZ71oJTajXzIXfkHLN10S3hwgkbmb2lN7XsSFi+oMxxeaQq/C78cGE0eaOtV5dUaMf+bdWL609BEbNJB2sb6VKqLU5d7dDFSb1u+9f671gkl8RmPuz/OFWBzPsXSy2t/2ekgSKHdJg5LaScSa5WKig5RZU5T+MTpc5Y/J+2Aki1HyB6xjdz+NV62LpVFTkQJqqXqeoEnY6nWL/QBCStmm97lAr2k8a99wkLSFpkzzIt8CrBiioRNKKV/QFJdKG54XxIMtpMuss8A0IEje5+O+fMY03EIU9QlI1Ou5bhaEBSGGia55yCm5kBAU6ZUWFxUpAFM2DVQMTM+SogZiiCYs3LomGSUiZeREimMB8HNNrOTQDZ8Zqu2yhh6nzMmDHn+bjH40HNb7/+2/n+/Xz/pvNd5y/3899i/tn9Tkxw3g6CIiK7B8yYcxzd/TgqSFpVgSrBVCZWY4rb2DNFgJrqyeXpC+HBhdf1NVFpfT+niUtltmElXJtK655F9IZ3GogMlYIIE30KTfYEG6y77QUB/TevBbPk5TiO436//yOUXhbw8PXr1yV0+tmsP4CxcuvkfXT1DTmwryKvImh0DN6Kvgtdbdw2LVbJr+/lgfV96TPaIcSq9VmC4TEBVKsQmbVQ1Vsn3Dq679jPuFTAMjlgadKthCa15M3oi1pTrogwL3Y7CXeagzbM4+E0w/v7GTMxdjMDq2xQoHPyj8454xGrFxcQs2LLCkyNNAQ0oTONczw0PYyHMGGCnZKDZinXLyIm5kSOWcvq9fAcH4kIhGQm0rM1TBQmEe0fpCwOM4Q5H+/n4/F4/zYfjzjv799/ff/25zi/U3fqu/Ar+Cvte8TdPcP2dZHp5uUESANGzrAxHoebwY0+zK1krC1nWhrz/JRHf2L7oEOBugzri66WhS0kXxdx/dt+ZHGQMgEsemib9QpWMmLoTrSkomWHuTIaAM3NAp9l6PnvstZjuPAfZKzp/mHCzP7z32Q9Ho8xxvMduLJKrO6ElLTDs436VGusZ47NEUzUBVrPUeG218iqi8mnFY51Al72FdtDizr7S0G6aSrr8qiNvEAnAMsnNmHdrS9MAMVEDxok7jbg5ywB26aNhqsTNd8TPaqQ5pb6RAdh5sabWYWuj0doljMbHPm1UqKHoNFFuSWBNqXuFtNEqbCZhBqTkQoja9jtJJLQBJ6CGxxhlEFEnIxgCDAHBbOQTRDggB5yd4zB8wHMSnxsSjNlL2NOxak4Z5yPx/es987393nez/v3eHxDfBffye9m74YHdOZlzPFcVgWO4li58Ug9DINRSQEygw+O4UZzr1cs75OOCaqPjcgY3CpdyCsy07uDut4PtHN+Uqba8T00rJfPMskSW+3bdCsqWESsQaRSMgICzTutQvpnfex/35W+eR8vg2aL/K2+Yq80REREpD9YgYX90cfc/4VrAChmRD6+pfHkPQYga3iryCnCzSybe8sofwzwPyypxrb0Lz+sHEyPZKvkfEFAkUoAbE0CoREhpuVmDQxEoQpXRXEZAilihqeaQRPeCQ6zAHIuWULmOZ0SJhwG0M3MOMY8HwFZzGT3UEKas4UmW50ehoAQjU6mFiYRAIXJYIAQnCLyY05UiQAxQzQ55BRNifMImhnoU2TmRgjzkQKeVBgdJAwRk0gyz6TOeb7P8/1xfr+///s8v0c84nGf5z3iYXzAH8A7eTc+gDBLnVCifEAWnfPC220ceSrMRcocbf1HckMtBSo6CzNf5nhdiIXaFSyz7pnEEld5oEGbp4u4JXPLzWNhgf3mpea4Q5SQZoqZmq0hd8j9qXvrr3l0Xut/d2X11cze399JJuL6N7TFH2q5WYXOMQYtdG9L/+cTFn73NS43WMXeCpM6sCqkO6Mllr6WOrN+ekR7HsDF8lwbqaduGyuBzVso1SaXFwKEZA4aWaXV2gpUwxOXlUcyOpB81n377epXyJ8VC7M0Fl00lZQwR2QalL8ZSffkOJ58MB5nXAkQBDKCkQzNgm86Fk4mvpoTKgIBnXbB+oQ8AGBCXmzJOCHLAebOpsRI0gQdGEIwiUyakDOm6DSHgZyGe+iuuJ+Pb4/57fH4JeK7zl9jfot4j/mI+TDKXFCAEzgz3lKEm5l5ICzZUDQ3z3Y/dx/DSdCUH7chdyOV4X9eivQcTQfIil8Rgyq768uyP3i88J/dYXzEDPPN6089q4DZJdDyJLN5yfWhvmfYnYCVnZB2hRJ/u5Dztf7ytWL/2+2Wr0g6z/NvRRJNK//t27evX78CeHt7W4Z+iVeTPI7jE4L+H9ZgJ88AerAvVmybHJWKx5v64/WgGhARM+XaVeQUm/OC2Pb8Pb8oeoT3wgcSw80awIKjmDpjkjCREo9lYpBou67yHWmGHkqslhftHTB1c+n6SgGEJTMkwR8UG91pFmYWNIePcQ5/3ENOeggPVUEkIoR2l2ZgDkoAMl0yq0HCJBNuXiV0xgQfOcAmsRsojEPZeCdjzpIJAKmTVONXKCEmOTADcigiJmGicTiYBP/vcX6b5y/z/DPwXfMb5q/UnfFwho+Awm3V+I0GgvBwesb/ZrTk+tOlnKhDM7nXtBZYuMuH5bVIoCwb0/JnYCGB6hEi6kuDFeBH7FWiomHkHbIkxtY9uuVzVxVhC/xPFAkr+w+sOkjUQSXZMKBLM3sjrMr5LwjoH2Kx07oFBKWlXkDNShr2ROE3MZxVafjTn/60BD5/NPSS3t/f/zuP6Z9jDQCVmOcJ3Z63juAKN005skI3OlAj05JmXqCOyIAtA1hoSdnbJHIkdX3F/IUBZbG5AsnmhSQNERk8d/0XAJqnGlcBg5fKmBRTNa/4yjaQbVjJVCl3wq5gEHIiGYxMrTQf8wE+aI7z1DwFQo9ZheUWR0vLlkdZ2m05gF0ruwIxG0yrNjdyOL3OCkkFYdyqmHl6AUEyDIbEk/AIy0OVCJl4hu4xvyG+c/4y+P2cf5bege+ewzrF5bIAtgSIpJOENaVyDDezkSKAkJs824CzQk6AGsPcPcN8W4MTuizEViiKyFp1ofboRLDPUgKDVwL+ARvkxg/uZCJDBwfDzPYBv/s9tnBkY+pZGJTDAZKuCoCz0ojMvF7rH2UtNZ455/IE6776TawmY/l8Z04jsJantk8p7/xfXePCaHotq42KtZJG46gJUml9Ly3iiJ6/WrKgQJd09KEOvLn6elDrtULwBVHpilAzPbYBI7wagth1hWrtxJM+zFKbANO8gctfFJLQB4zyMSKstNCK4EP0LBYzuHNMPu7zdJ0PmQ3JIHs88k5lNifk/zzHYsEkzuiR6AjlXPI8ZxIxhAm7gbraFMwIU6irqqIUCkLQGRN0zzJMFPuRCsV8TN2FO/Dd+Avxq+yb8ZwWZuE+Vt4VkdKZDoDSDCmdHUnC3Ia5m2ezlDtoMRw+CK48ILXh2ND/asYu69/PbV2LvM/65wTrrpvkcswbbPj8YkGDcyaZp4oHnTvutxVWSXm5nSwp1eudmgDr9vsbPkqv9deuxcRfbVm7RUrGzg4TzTnz1+UG0ubkOz85uP8XrmoE00baabAVEZMUGLQ0T82wLsZOv7+3tWK4HclVlfvKfAPF3NmzBElJ1Sy0uPpgqw65wrsrAUgrHUAVZneUafkAohOFBGIgTfV8sf72bH3O1jQAUXphKPJjz1TBQEyMw+ap88Q8FUEFxuEhNjc0e4/zEINmgiurGTrP84yw84QxTAaJlo4goGE2cvIJVmEbBobbkOQQeNZpy1G9bgZBEwhRsgmdM+7gd+MvM345/MwqtBtYY7L8urglLc2BQ8n6NxYHtCZaGll60WD4YaS680t9nhP7KatKghz9xKalbp3x9gQqpO4qFK8Lt+J9/EAleNIOqVsnEoFsE359thar0czMu6tapViOFhVCDkp7rX+gtbCaNN/HcSSHR03d+c1PpbdYvkHScRzJBH0lAf/5GtrQc9UgF2CFbJSq8zZHNQElybvMLhty1Sr6aSX527qe4X5nrogw5tCSFScCUsa2RhP6KY0yASwwo5gliwfYe759aVYuqrCMVEXbYa4KNLooCCjitNK9q4qDuykQBnfE4BE2J2IqAtKIWXCNECzHmZz6ZB8mfGlzxgzMKcUJOTnUbQ/ZvJSGNRXqFZrxMNY0wwSjhJMMGM3uYoQmEOQMTdhk8n/iu/hufman3kEH1CN+EvKHMmJKZI2pkMxVYqHBzNysyhsGMszhvlpBcmu0grpMqxtrE3OtUkvdSgnW57O9aPud/F1Qz6XNks88qyRAVi9v0vgCYJqFPMzrdq1MKyOAvP2CvCZ/RUSPoIh1Y7zWP+xa5vtDrIC28r/5qSVE+rL+/8s1Fhn7Amo2hTWpB7bUqF5WNbP1n5NwvR70lVR/MPdM0Zpn4vVC98Aqyi48iuZAsJ/YAmqqVwihAC9Lv6zJBzuyvqJmmjcGvv6KSzBuw6AZqlEhyQ5NCAxGyWgySTaFksxkzAKqQsU1TDpSxb9iBCNgLtP0mVAMoYciFCPlUQkqwujdpgCXFA/hBCIFIMSTnOYUI3AyThrISQQ1EZOYiDuQM3uBVeUsxhPRe+Y13SV9oiU47pQ5i+FjWmB+zoL04s/RzBQgswlMYhZAVtZ1XVlgEbQydbC0zipKFzsHq/reIodECQLGdqXQITy7N7jHyHQn4VItJLEmC0TLU+f8ZHbtN53WywP8060MUv9xNIX+2ddI7mZa/pWVZ0wWkdwfmjWKrqwHVLS7np9kX0TI7HIMPSNMXXnketxWipBBdlf5rkgwn1Ami6ZHRfbTi+R1sPdHJaZ/0cwLxkk6Tn1j/X+0h8h9Jq1as9ZiY0lamMNM72PmZISUQ0ey9qEDAN1MKdtfQxBnRECe3zunQvIQkH3UQcw5I5uFAZFTMk0mCK9Qpb0M4OwMYDL7wSDDnDEJ2KAUMx7pD5TsLFZUHxESKc4QArYkOrgBdnW9RGJ4z/SljGIqcTMD/Ovi2MiO6ABlTcvdyjNdXCEuLK5LwT1/egUc0f89CUP1p55AoTyxlgJ16HkVkFYytfwKUmD1CdXMN3QckM795QD++dbe6v9af+UaZNIYL+7dbkYTgo+Q16TydBFpYKTCWJ6wl6QGpZQkMDttf0Jp1zNZkLDWNy87LTUlqdwJsJxE6c6g4eSM53Dhub2dIpBZGwsu7RoARci52GZST0hJsWulwIPMrbp8MzkpdFsZRYdkLGFoK+khRMw5czyWQWaDClMRqBghmiJEnQojp8DUqnEbaaWiRjnO0IO2YPRJo9Gm5NlklmC3g3D0ZBi1/lKKakAmRPb2pqdEYfQXeGIGc7Stz7p51j+k1HoDskjQZy+qYq6oAIE4Z5QcxDNW2zV5dTq4QP9YaE+zPvJ+eZ7ws902y20k2eyDqOeKNuoClxBQbEd6/Qklovpa/3xLi0XyWn/dGoCMnN2uudhXWxCXj0q4uzq85yJyCBWmEUhrm2T/jNJXEH0V+opzBCttljR2XbBNm1514ooDM5CsUBQRAoNsFaAaXRhq+nyuK/MgQpGVv3zek8xvNdhyDRxPLqyuEgULR5YmMIVUlEsnIAJmiIBLYBDzSpEkY9TEBDBnRsoSDgLqhEwFFDOLFxKS6UOMPDTTTIbtOR9V+mYAQXPmzPjeVIbUUpgB7imxF5HjdAmQ5u6eAnUrGt6NYdV7va9VYv1b/E4uDIft0dMWi8xJxJKqhTh1gUD0gK5LAmSljHuYv8C6Tt1q6PTaww/h3oolMsv8gDquqL9bDZSlLBTrVDsAuDEYXuufZj0eD3xK4bb/jjVCk0UQfGJks5ndhpSg7BEKDcQvFCHnbigf5m2gI9CYfhZsuxe/WRnsGbJCVF//Xh3OSFyZ5lML0d88v2pTqbesAPYpsivGBABY6kgnvgT03JYUkPaqaKZLuDIRsyxdJhQTynynPFGnOJYZSR5zuS0yzFXMRWTsT4WlbjMZEac5kTBm1rllaHfJQs+CtMGzqigMQZbuLlIROsCF4BkoZtSvqkqzSusCLJvOpBDCS+RZ1YpVIj/IHMg2Xc9urq7m3mWKs8o957lIWSh9ty5ipGUvpq4WhNPWuagHHfVjG/MbjQJumWIsL7LgoAUK/YYRX84g96XLAFgfR2tMv9ZrfeY1kpytH3LhBZ7uT5i60wlTFe2yx8fSEnOXoixkaYxhTQLYIIBqHD3P5NoDYGri1+PdaUXa/NYPTiAoftCNWkNxq1nUatIAOufgkhJA6cFlJ0FWRAsyr1caPkLZnRouaFV4sGQMleGjgYm6RLGVMtSt5KOa5JrTmoZYEunOMoiltqoIowkT5S7Kkzkt65gNi6XWAqs0kqo2dXJCEoU5w52QYclmZAe2goYIMKfLEIpqk04WUDSiTlYwLxT9ltWPwWYAlzUXgqj6La3KD32uddV1VDOKqyIM5qiA8q8AKcKal/kBLyR3lLAKSDnJrt7cXqH894c+0t4OVl2h7+1XFPlan3oNkoocvdv4xobVqFkWHeCKMEWwS6UgTFaEihZVWDFaYGlCp/QlFi0kQmSyTgUQUbEti5HJ9eZF9m+1r9qxZASm62oh4gvNr8CzpSOSlJk7v3IcJaLfzbrV66Da4FMUGenmuuu1CY5dwUaSLNH4e+NIbboi0oWQU0rDmgcuIbEaBCdYbsZF5iiGyIC8JppFVKCd1tSoJWiQuUsek/vISkNfvhChcyaRXw5BZlH4WvFqQcLL2NdtwDoPxeHPk7lSQ+S1yV1gtjBznauM5a3Gbio9E/K0ryrMpt8pKR0YerpkfUGfSWkCT4XiheBv1r9Dlro9QPZ5llRDg9GXZqcYvdZrfcY1QoKuBvorBu8sXiswz6dTWXgMkl78vyrHLcNXeg7MVrJlVdkbX82ZVeAFUvS5ovRYlQBLMCGLCUDi67U/1SjIMqIo+a8qQVcMWO21tT85Vr3agTKilbgZCwo1VD0tY5+KoNW88nJ6rEAVQKIyeVgRU0qZh4T/809Z5BAsusBRFVkpIuRMwc+U38kZJi1etioo6QQKVc+ZbEXvLyyuW9A69yg9ZKGmBJsrSy/V+2bVfMcLnRec7fh7KzQYmt3EzTpXAQDwBtMTkEsfEDBLn710JkDUqc8ifk0eRt9sq05wrfbB0W6gVsRMoSG080ClXpWZqRPQKqivPOTSJL+8+2u91qddQ4oVvW40nHyunnVaGpxZ0OksPZbMHRj9lGa0l5H0tjUmrP9BBf5Z1OVjTIcVrUkr8MayTdmpYFWWKHGeC6AvIEpdflRvpWw9VuGxypFZ27ZyJojCrogieqJsCqqgXBavjndhEVtoKSk5NuZ5sLlzNZYEKOjcqEAaKnfsMEUa9DSDSZrtS2ErpA2pLyGI7J7LTCoI0AGUDM46t6xJiimcV7gWsGLkJ8C9sLWnpmtUQ0M5xTrbVuUaFE1MXae5LvDy5uUY2spD1XWR37JSvTpFWwBRlYMV2ne6IkChMLrZ2pr6JHPdVitPeGUAr/XJ12jYB504OzakNd+0p9irHNdPaXJC0O3aLcTb9oXNJc2Hdz3JC2Ahl7BPbrYNX9spJuXlii/bBi93gVKkEwV081aF5xm6BnkFjN1B2ksNTCfGxYVirX5mLr/SnbqzqgUFWSSdUcVW7fNJVjrSjFZJpQ2a6ceaZJupiBXEMWkBrA6J2oFsv8gRA2lAM35PmY3yBBHF9lRNYY+I9M01NgFY9Ymn81y/rhJInZ9rakdfxJS7yM1HasTmwWJ161b3hGX3L5knAdJS+0ucvtQnyi1nbWCdQEehdFqYz7rx2CZ8GfBOJdGjCbBy04wW1s2GV+D/Wq/Va7A0nwEAxAV0dLSVa7mEtWZMa5VNrUCP9WNuZ308zQs2oDaN4wIsUM+2cQsJ2cHg5Qw65AtN9vdZPfwKRVYhd13rJpvP34K5tpQCO+0kVrDfljELvFm9uPoJzLNhdc6JhhdES5XQtFuzD60cp5uVnJImIDNH4WzoMkkIYQSagZNpiRlnTWl+xjCad39dslCelgDcXYIndqbqhv0Q/DZGk2bf1huWnwbKDfVwBfSlxCo8qIrtkyWM2oF513Ra0iOJQRsEo0KE0q2tZjp2pthk/2oX6FsoWhYiKUkWcaJBJBYFIMy8wZ+SL01Ykj9Mp3it1/qEa7RpRUeaWE9LvmO3+9gseGf3C6hJIk0FjGmYMjItyJfAFnwRRno36SjtDrBkvwBs6g0LeiZ9EBfHnqU8maPYterQK9bLL+0y42bXekRMfUtIWccU5jKOKg0DAg7OPNyIWA3PWOoEjYJvqEWVDbqvghWnZ7irkrhQaRXsvWYLr8hDz80WQVNI7KvPYurl9dnWXhpFpQVEFYQ1qxSbbfR7OCzU/hjtmpsJ9TaDCK8DATYsaOUKBSK2ae8rYGJkYrSNmbOYUSJOfdOxu8NqM33vLTezvA5aa3Y/S30TSrgyToBZlUGJHXnnc+jM4AUBvdanXqOp21lFvUgsH/S4cSH/Zbh3EkVCyQCb6ZcYiy2dISlKeqGBb7UIUXZjMRV+ugrBNCHLrGyRWuIpbRwLc1As5H1ZkAVh88OjHhHVf6AEuD0KvKqBjfjYUIqOxJFKO5uFQhpwIGvGFd4mzpOWunhKTAEDBarLScW9LQm5tNAAASN8FdXLPuc4MGIJ4aGG9DS0oz5LmZqYae0kYGZzzgtAe0qDlr9MelKqBhUQVFPXa/zWjssDFxIYNVNgdV3giiX44+UjNlLpwtmkWBSkdc2rPn+RyTZJCpLcYo666isl7Zuwc5HcTgoNVWLxygBe65OvgYTFBenSdehHC+tp6ngXHY5dMusqnB0V/+2tWFsBkI0SWE+8bahH5Q+6pTXrB4KI6i3gbhgAFYen96c2X32qqo1HI0jXQW39RI30N9CwkpwPQeHlOZQbx55epGZZHfXKFtLQa1pRcTJcNZKhssJllcpsX+MqKwloamZ/VzOcoHTVVPpLduif42Ww23e1en46AjNrsSD0G67LCmSyVhcTXapVv5ncfEye8IVAISvqIVxnW90rsC7B0w+SAZZEoCyBPEnMAlf4r0bwU0gu3duFYm1ZHdfttiQx1oW97sNMTF56Mq/1WjkTeKm0N7S9WWZwMeJxwTixPX75QfaI187/C/+lQgUvEKVGqf4WsBL/kJnFPEFYMzDTQm4RYhkI4xXbVg2h6od2QSYVd18R7odsZs8J2vpnSXYVFbB/NcCtLHkZvv1UtrFLAG1BNxxjrBO6exdJQmpadOxa1BrlIExdx5jcUMgEImXgctfLxHPyCv3btFEJlJcqqWWvVdn41fjNbMuzAUqsdIRYo8mWee0boKiW9SUp5DOG9xsu6adoNcAPsi1SNT1D8mqNZg0bqrNkbFee4f+y+6szWVqKEVyvNJzY8FNOTPt4gaBt/SfPxmu91h9+DTaNpB4hJjKx6rQA6O4pmbl7BqBs+PabKmRUTRTJxALNAAHQPb1E80GL718E/FB+v5X7kDG7lH7o/mV/VT352QjGzAhkdSArts8v7sEvqDnDzitCREfcywYteQxtB51wWQnSZUEY7ZzaolyB93I8DTol0G+p/N8004te2ocYUa1nnD3IUOlAeQHxVjLIq0HMO0hfMFohbD1DsYo9u+xHSr8td1bmPpvIwOVuV2bT2QCrJZsOazcMtOBdnYmLRNRBPiTmfErDhQdmm3L3lKNvi4jZ4UW5d6MHpq4rInUv3roDI6Y72xMzQt3RjfYTWRR5FYFf67OvQaMXhabwbGyZeD+66Jm3WrH/Ps8PFTZe+EChGSVzA0mW7Qaha8LvGgsskAhAQlCLHVTb3S3I9jOZbJ9lmeshX1XQrEekWeu0AxKNEOFl/ZfyHciU2VmYchqLopOvwmNhNyXX3IlMH/ZmUS53JcU1a8y6JiEqUH1wjD2zSaO20rI61d0MVyCYVf7UUvhQdVRk0F2AG1nHyKT6FOQj4Uk6Gxd6k5R/Ndc+sxPt6Qurvl1OKz8e0TO7MwQHVyH4SqRU7j+xQeX4sw9lZC2SbjljCZ5FleIqqImw2m4MLZynE1lUoUWZm15VhP62Kwt8rdf6nGtsxUOiJtOinuBNPHkl+HvuzAozGYo0RxHzMjIbSkLSrXQGULEiDFl7uHR16nNKxQXV97ZxxmX94wII6q3qbiYQS/fNuM39ZvdVtWXsdGUPA9uZqFVlikjTljTr1SpvlZ1Wdp2cFP18LjxkXsVSIE33KpJzlpEi2aDI1V2xH/JyT2rrT16KRsksnTMJkVeVwpigylZUzYbrjL7jCrc/LFbrRkb8SGHqC6VC4kK2qLHszCBTjtLe7KbcOlG4TG+sWrGy3qwkwApaxOK1L9f/zxoArBx/nU9VNbnKJ+WZUNY/L9lKZ5MUlGJz/mO957Ve67OtsUgRANZzuqLCDj8zEMaq8qVwQ6Eq6k5igl7tUUQ9vNaNYMBSAQNaUyGDQBWJBQApqznA+dYi6LDtY3M3e6mootSV02vFnqgQuoaIbdzWigf3ydHF1ww0mtGS950crGFS6XLMGOKMcHoefn6vdS7Upiq5UfSSiAgW96nhjhrWWH5r7Vuvy/Otq1Ou8upLqNxieceIWJDOvi1JKaE8hmPRQ5fHsuvqq8G7CzyXcmRZCkxguy5NY8Xi5LCHiHaLYachQI4dqE2vmRBW+NSWWXLtfgFoheuonZS3mttVK2rV0g/Stnv0UBd3SzVe67U+6brGKK+sGQBwBZJoPMTMMzbtMqO0HuK0kkYEimpOeFmHtIUo6XitgY4QLNH6heIg+dplN8UcArCCd9Ldk06qksu/Au3abMMCLA+TVcsKrGNVIKoocBEQxxjJEzdLcClynjBJMxcmFakq0To/ZO5u7z2BnGycs3yT/1PiF1XuIGqQVu2YWXJqV/UhXR7ybKvFL6rRGSSCqnYsda0995/cZRWuwyz2UVw50/Il0rq46UGjUzHu2yl/3CWhiECJ7q2Tv7KolZMhqg6ULgadgax6Qjo8xEUoWCSoy/1cWVRnkqg0pOGpgsjWpdxjhctbW0tk54FsDuOvf4Je67X+iVc5AFb3fsXdC9NYAPQyCvnDahiWKtzu/KBYQ0pOewEuLQEk0AxN9087tsJJbJjJhRPt0ehlXiQpzmvH1rMO7GH+FvM94wozy6bLGnZVYwO4VL7HSldHCJopovKdbOyCdA08AQBFwLlekeR0EDUCjFbtcurhlrTMatTCD6S3yqlV3TU/X+caKaKW7qQDdDZwVCeKTLL9ApGkFknugL29ywLXn8sh2/lMdZDlc9CyEOjzfA3X3Q03EoXbXp0NDHaak24w753L+2xXKzoWwbZ9tWtHX7Xa1rbzVyjT/2rdHevQPqRHr/Van22tmcD59KHmXi1CO9Ha+tdT2Ey/haYv3OCiokvKp5eWnaVaUAb626Ll5/enUJJXi39CPQsWx5r3smLYtcEORHe+Th4AYpb+G3YgBdSsGcLojtkVxuIybT1cvCqha9+byITGxduF1LGvNoOMVReOlUa05xRmSpQRelwyb9XjmlbXLtwM+SfSIqedl7kHyYA1xbZmK9bf9rP0oYST+Q3t+lPmfCrEDs8XZjn0dlfrvKdzepqxvq5PR+QLEMtgIQlixmon6XFAezaItZ9X/LH/bcsV+gZsk755oCfPzwVI9Qde67U+87ogIBRRvBtUseiGwBbPKsu2ba3YpcgV+S1tsn7Kag5fItRcag8AElZorKLR+zXYllXXrYQDbsk6rQJvw82r9Fe2OH1Ahu9JAUwxskaTy2GkS0jzt7IcbkBKGp05Z6L6Ofarz0PUgAODZYxeVQGLGbWHRgWsipBLPkib5Soz2naup5SVAFz+8ZqLQJbJXx4JyzhWhbPA8Xg2fB8C3o0DGom8X8ebpViIhFvK2xWpP9+WG12K/6jQOq1/5n92+fXWxssGtKpwoAUvPu6hRen/6HLoAi6urTpJ+og4RWR79tpUHs7HmbEsCEjPr7zWa33eNS6UpJX0nx/MDVK4kAEVTZBko7zLvHKFh9fTuNoyS5NloTmzfIPl7xHh1bgECU6q+hIaGpCiOI5Knrharqe1Ewqg19VApITU94dfQENY3Pb8MhDEUj6wMnGUmSWxNSNJrKp4ThguberqR1WzMNc5LHpMfbmZeTUryKxG2qc3XXNuiSVX0FI5lcqgdjpHIqsKL1hzstZX7/YuYZ9LCKiNbZ6/iCnNkLF0RjOVy5lfVyTOSN5qeso8wyuXKrMbMUX2ZAd0AT0UIVW+lb0UffbZGV9Th8DtFlrBBrFVpzqQN3aXR78/ukShzYXYjo/9mFK81mt9wjWeMucsYzYX8okg1KuxgkYfAKRUWWPNvTX2I3rFYhfGAqHpH/V66zTM1k4ANHN+SdmqYufwqt1dj/cSfWxqYKs+ZPUvG812z9bAfRUpisRSIXqxYeodPXA4bavWFlC+rYqKMHJezEi7UGgoZSkBSjP9htFKtFPXfOJN0DgNrGMNekwwrWGW3NE1ziy/aPUA/4hvrGh64TofUoScDSlJmrTRaUBF/bQu5avp9tZnrhKgp4trxp3JVHubY3QoIYxZTs8rb+Bq3+CWZq2LYOue6d3elAohRhV+5jVPOJaHKBgKASzxwZXOvnzAa33qNbbn5OPa9ZOvMPYZP9VTafFpUwtU2UxDdri2RXtmm6xf63srTKzQ7oOcQEINy1K0v0mdztXfVEZ436Xek1US2NKC5b16h43WE1yYuJfRq0uWBgoJm3Sxc1sZqq9jr8JAlnOjEHAp+5+RPXHJgrc12gXF689GPN/FLXiRq0CU7CiIKlP3N676SP/E9RlAuMZelngczVMKroPuoptmRT97GTJQT/9gvCQloGtmAPqqNLZWnNRVaxGA1ojlUgLqg161hA8hyMr21hChvq7lMDtX6UtQN2qnU1cKKG0t06/1Wp92lRgcf5BHX8gMfoiVMgZfQRb2BMJ+E3jtiPuCm1PK0niZyKePFI50wdcLollSCeUqnuGm/NMC9L0sHQrFyd0rEU0iFEkHQpVbrXQwsHTtFyTBQsNxNSIwoYcsFcQENv5KAlpdAUWCMzAjo+lP3U2GlutZp6JH6lzhsD1ZyITrNzmHfDENc7mrxb7kFkHn9nKwTJUtQJDOkKIHckHaJj2kZy0KL7oMhE7/KmHqA285wa5d1P2QaUKPt+xRz72RWO1a1w3TycF+E+5XuW82KaYy9r9Ck2Xrle7k+eyhgwr80HLxWq/1udboBwb4QSKxBJw3WTSVnWIHhvXmBcvsicT6FICNsqJ+ODOEfgrxtpwAHXerTTwbRbneYLYi0Nq7tfvl1TQLN4ItVTs2/JIfhBWAQyR0M3GR32fOESEse45KMGNFnloE1wKwI6opWjndpvGc8hcEYqLgjDouCOa+Srhtt9GE2nV4fXpJgu5X/lQXrtX/1WWD2qIZxDkngJJ4u9KvdVVhWWAnczrxBjVhDSEITFbdt/4vAa+GiUgaMpYAHZ6QXp1zFsWI5pDOWaNymtoEbFZ+ZZl9F6HHuzfqRBZJ1mw+DyBiNwwuZ7D7zv022+U6Xuu1PuEacyY8jf15A9D2dyXdsVNlVsNns0hXrKdIA/dDXN8UkpzI+Bsj+vLX9ScsAHoBwVrtVLVW3399w4+Qbs0xX2HgmlyPxpgKX7I+2MXCLC/VY9OFJKsHL5GMguCr9W0vS177VKPbtx0G2c1Lz+FtnkwzTxG03slyWqWMZt2GsIx4mf1VL1Vs5613A1L6qez20hQJgzIbkBRghKZxdPFUrMn17fmUqVxBTATn0txmTe3avAtUMrMN410nZcf32XI9HwcQ/ZjSYYGQxUqKqWA3eS1XAaS3wH5RlkLUWmubr/Van3YZWZSP+v0qJCL/Vc/h28zrFWrhQoTKtH0cT3i9DQs8+WD61zulC9lAwRbNxJciCsnZTYO6RZWEuy/b0UIOZfSfm7zqu9Isoqk5++FUE61V1J5m/dqxRIyYKs1PW2amBZW7xEoXcgtmzpxMSDe6VGPbl7tFsYA6/L5OYJ2x0lhu4nwhO4mVmCN73OzyQS02l6XsKtfmx6Q+nQ3pXFUfq5EMF0reB5hvV0nhEVUQ7twuiiK83Be6cznmbBcFUBXAV7IVP9xj1y20+4M6qzVSrarTKz3dvenzp9BOfV2+6858rdf6tGt0GTWWetfWFaVVXUx5r9Bkjip5ip70jNU8dWnNOZ/j/RJgEWYHjjvsKwDneebmtZRoAAeKKgn0R1Tqk4XkEtcjnTsfy4Ls6FaHt5t/KptRFjyhm2R8qkxesCL/5cCwVA7WZq0x9IgUSHrCMZC8yBwA0AAGbWH3BS4lkShVEqq8iVJ6yL3N0qjKxjWkreWWlhpP5T3tDhMI8zw1qOykVduYxyuopdxq24oIcyqVq/uac5V/Ld1RZwzIXC/LxT3cpoWY1N6Dy+tdFrkQuS3LvPKqjuLr0LLDMIdKRs1vyGp5nec5p7uvW+7yzj/EHK/1Wp95DSnm7Dpq9W2qAZjiKVYjVGPdWWJsb2Erro84M9RaFr/jr7nA1vU8Q6o4rq3knhMsfHm5GpLSXKl9v/96sPM1MyZk0ZIvWCDVFcwWHN/IElb7W3LeK/qXAjAmBGEs5Z0+3guR3x1YnsU+m5lfZWy7DV0oIX9VUrPCZVTVszTaWni0/NLlrjLPWeettx97opDQ3bL1RRoqLlMASMfMcu1OOBDSSSvkTRUCqLT8WAkEy8ktJF2pbEFmDSC1AaPuJ+TZRQk3JWGJ2A65HFj1olQO9BTIr9uGgBfiX61hc4ZZHtFF80eVTxYbCis92O1+nq6/7DF5rdf6Y64BImaQdPcui6pFFjOcFWVqt4BLNOhSGduy6trAZtP1RMBbMG4JAYXRnz78UcxHVl+w+ktXWN37eHWHcunLF/TRb+sAWY1QJzgzgCQ+pi51FoHLYFQIDEmyy0yvf69D+uGH/CwVCu5/KgeR9i2iaq4JtAFm5ikNVOaf+barSPMff+M6FWyHYQDRDN3Ll7N6kjfDmiOIBzmxhpOhqKCC5oyFz9fh6zcOuaIGZLubUlqcbOn/WE0hT0dBLorONd13DwjWgbOzQpabL/Qvbxl0UtKuXYsqWtWIrkU/Hzte67U+8xqdi6saXGvaLiUl197sMlzVjQssKgmW5cWe44u7We/Wsqfsu3yI/5iSN1zDbiHe36Cu8u3GPUO/UDXfVvDabB90JmGlUFdFC7D6TyUszqva+D5H3IYuuLJPxROesMzKVrHMQiU2V1G5T5wys66FdkzdcFca4OUXGlV7anCr87rpca5BjOg4HLj2rbZ6Gdal2cCq4ipyan3NZcTylVhh8oJTCN8OOV/JCD1wFZBBInX+t7ztQpFWatee6ZqethEBrlsCgGL5091D7Fdq5WfK+2GHHyOip35iBQH/6dPxWq/1B1/DQHMDMu4sRHgHN6RL/x01I8USDW/ts3xnx25bwFV/wFPMuDa7eCzajPT6a74RUEi+D3xXpCj02nw/7bGGWJl5C+kwYm6TQ2o77iNxA0nCNfBEy/xl4G/WBja49Dh719HRZVtwYPeCfboao1fnVgHhfJzu4zJhaSwjVt1gnYqe8cJdY4eFopS1NaOZzTlTQ3R9ly0liY6v+6ySNHfvUrmkmDOwMXyUkyOJdbAJNa0QvpMk+rpqbGmn+pCv8aIZrS9cb0/O9rW71R9frMOqKQjXqOEVHCx0bn103V3o4lbeMHPODSZ6rdf6pKsawUI1ExL1kOdfi2u9Y7YrxtQPD15jNkLJM1S/zz6gAx8j+g92vzL9JLlLmJGQxUyEBA1591egcellNstY5CsxCxHeySxdebYrmGbKM7igmYVu2sIrSK7AtoLuDvF50ZSueSdo8ky+W8iiaFsr9Pyy/C1pP8vr9oH0dhLFyTIxe+cLC9mdUB2ymZJs2qWU+iKGepL8nLGd8AWXIb/rt8Rf62izmpL+DxuPs6w51bCbSEPNOPM1Srr74pR4/fLx605Yhfq9et87FusY84c9tNfW0f3DfXUlZ9cNfHnrq6T0Wq/1CddoQ1FmvTiG1wPTU1nTXgTQVmOzEZdz2H9YVrje9AOE/eEh3/+6kA13RuiKyS/YF+7pV+IJPoZYynJlhbepT8Iyo0hxSpKWld4yfEGr8ePacAZBTYTvA5ZqLDCZhYdOkRpoBsKMEQWoN87Paji43nxhX+1A2DmQVA20NUYxv6M72iQ9TTQjCfak5VTboali7eskm1mb8jV7S6R102/mENGXr6og1/lruOq6+qXhKZR207VLsS5eh/DZcdHbuBz2ur7YVoYR2YPy452zbiqSEXloC1DiIkGtdvGVWeaLmV3htV7rE6+x7DKZxMON8UKTZpPEF32z/0l1Yu2ht0XMjLrSbs6YlhSiZ3XMPZrb9yZfWcqdJHr2LXh9fDmVspGrVMCGpFHliuUt1PL4SxVSl3Am0EcWi4uydrUj3DLE/Y1A6RFdO9+WLnGQq4+svVGpGpTiggDQzPNYoobClG8psX8VAbdrD2LJnHnEU/NU++BqSjC2iW7YZfOpV97QVy3PWCwLbLbm3QtP719Hiu2E9/eXxMV6s9hTM5+czWaIF7Sz9mr5g30M0bpbPsTv/dfyDrnZUh3vuIVct66Wg+GTRsVrvdbnXSOkiJnG3YqlXs9TV8nUNNC0Q0o9nH7OUiQgTU8k6W49q4lsExc4wyYO7YH/ntEDltw+APV8FtR82fvNLl8rN8ne7bXzBdMjNUGLQajsvyq7X84grUlbsWV5EiFRlUyr4/XKUdIi9fG0Go8aFlsi29vseLZSDnsPFsS0quLRQfd+qtIHnOe5eP3LiaYVTAdc0wMgXKPHctn2hYUdLbSqtZ1ZXuqyxp0ZCCCVFMzakFYXYNnrYnkW9weUgXBKjJjLh65Ln0exkrPldK+aNjDnXLu99nwFAcslbI5K61RLStnydELotvC1A/+FZ+W1XusPtwYQ3e4qZHtSFn4T3+g+LTYROx9PLMNwMS7SMQCNM6zwjWR2kaWNyA6d/dlbCvVRfVdK4mXa2Bx1jg3uYM+YVHPuKwvpkmXuaRMB8y0fQCdKAcGteYpNz0z04BmTyAi5yuNqHKEicxpYs4HVCtfLGLH2StIiCNn2LRnjP0XTbdM/AGJXbXMVVLpnWDn/pkGtS5NOVZ0gaDWDrBxnkTJrJ9s7ozx8tDfNb5dkys6PhSAFZpyZLJaOc9vhbAIDQ7AerAmBooXOjAk66eqOtmuyMvoSWRYPej8VsaL7HTXCh17C5RFXwtNJSaBbPPZuktd6rc+8xsIrImEKGJ7PegAAFRFJREFUOiRgNlRyST1fwXun+RKsSThNkEyoRvsDWZ9dAPoPNUZtD78awJEiZO62aCU7Yttk03zCMwSekrmNjDxxBfj59oa1WtsB8upX3bgi6YQyZqyG2wYf2LZ1SaBJIs3WLKo+hP6NC7Le67QAor5X7aWecHASlU4t+YfYBvNuQFp/Xf6iDm/rG3nx4pFAV6vhp//uZrMNdQHFApqwhdUbmNYYOgBzb/cBCjkKzdwWSJOfT8udaaWVPivTkcKsFErx1B3W7sfMEDFDOYbelzjoIiP9eCP1K3U6+xDyXwNzHqd4tbC81mt93jU6+27+PyjNxPczcscGHFfID2ymJIGDVnqz1CrrMY0rVa/QjOuxzLUe2r35ixvlnmt+IDnnzMe4bZ+VOdXljVYa0Ubo8hMhzXOytY/MLMNzqeUErKaxbwBClUawjTkz88j57u1Mrmi9DwpIM1/a/gvWAAnzxexJLuOyYguzzs1s5yrdQGwmXm2+q+BMArA89hQdAtAD0NcJqfmUXTPfT3nC4rFEhkhPMClqygIicgTNOsa8SiCpiO7BtgJzFku1sjO2M2twH2jdvXb/6agDYNBWyrJpTG/A3G6718btkrXicp8ob1ChynUnb1/9Wq/1OddY8WShxQwSMfdmqIr9sfT3O/CTFClvs2CiGm2uDulKS7PnHS7Z3iuUXQIvuf3yIAqEwEaUn7gfe6eoOlZdVYQJQj2XGDnGROmBCgLPFRFWE7hakIckO6XpwL+LlsmTuQAKAFi1xQV894fNrCbhpv1NnMNS/e0pbu0DueQq94Ndx5jWf32kEwsqLi1q0mBzyyQu/AvLCALthuE+JEVktlctVNyKKKTFNf73sqcRAaGBQFF9q1BUt4a0UdbWFJJXfBUiNs9XXsHQunUxM63ZajCTW7NYfvDH+RPbX3+DddaA4XrbywG81qdeORQ+LfFkArQ9oRAoarw+xIpkRFkBqSGFqCFYuCYxFRNGSou1IAsseOGDtZKSNxIVrF1jI7XiyPz4Cmwbp67tZt9Aj4TtoH4WVl5tbiZAMUVDzedSpDpPj7FNBKo0yxa5ZYFhy3apFZ5VSE65yVVQyEnyq2FJapHNrca6jNqOaSwHkLIcLPrTSn2ElGRCpDdue8qmP2pDwNgR8XItti5pm+I6kxB4FaKXb26AP5XuIkjLajA3Sf28fQxOXoX/OSNHPVeQnjdGhwRLB8MrECkB72Ia11mt2QxAdOMCW8voCb7bb6c9V+j71lonatVRuP4KXA7ptV7rk6xRkDdotGXmLeN/qwlZ+4MEoBqbGvVZ22KJqdWLSQfarTNb22AlECvwT9Of3iey47+rkx2QFjzSkX6CCRkYsmPbtZMd7WZ6gWsae7a2zRk0BoJXuK0PaqO5d+ySMjfazI/Q8ZYt1ds2VCh3vbygkqZ5BaFXbF5J1TNLCsgKM1FqEEnh78MCsCkyAVgl+oXzLFl/FVi0WskkTFDSJNUC1yJIFOqlApcSQqnJ83sEnywv6zkzfT+UOERKA7mzu4D7HwMkXvNprFyLspet7osVqq/6+Tqv13TJZ8XZ/TSqFSDWeW5ECth6FVlkoadpSK/1Wp9hZQawkIoGOIwVrJvlBKsySQLUOu64HqRlsDo6VmL4H0wbEhtmqwtt6Hkixry6QLWn8ADMFqceKKOwrH/9mmaxa5iQErOO/sJlIre9vbQcahXbZ2tM7eh4GeXoQTQ/Ijn15jyELiND0tLaqwkzbIH+SpUSZF/tV4yIFjSu88O+VOvMLM+3ASNqjH7tV88A6Eu2gUKX2wClnPYlgg4i4kGKVopGRldPI+jDZMFEilTmrkEIQPWuwdYx9r99FKGInB0f6U0SU2OVKNCXaVE21TH7xzxSWwFpN+IfTqYVhegK8z9MJdpb6l7rtT7JGtujVUamVIILq9BmH7s9tSN3/UDZJirSszJ8XBCNmWWAB8QGeZOEGVd0uIXOWpSR9a2bV2CDAwjVLPB+ZyzjnMPWSaZ8UALpM31FVXHrRFypgyBeGqW6TCc7cF4ac7t/ynZTS1GdteX18c4eSq6UzWPdT+Yen3bqA1zWVv2N6nKrZRliy4GqON/BskiFHmuqcDbZZcf3ldIRF5auBQQRmL21QgLX4IKW1dwg+OINZBdxxNU5t5zWVrHBdudUANIlHCzYpyHBUMcNnV1dl+Y6XTvss15c/66Tk7njGDkJ40oc93vvtV7rkyzr2Fk9rDANK1cs369YUwC1xVO2t9Wgnk90Tw/A2FWAcjtPLGwmsL1AFbp5JyG2kTrW1tFW6Hq2CUhTNXyRy0noSbsBHQDmnph117G1x1thtXW1cre4W9byZIaa7lKIU75ePmbtaEjziVC7dQNwgfXWq80ZOjnLN6x96ZIGoocbpxW9Bh2nKhywRJDqPRHzaYPswvuyoVAsK5wtfqsfsPezW9DqhK6sr++EzgM2t72XClZ8vzuhZbrTMfThZ3Ovkd53Wo2GrsvevQLP9xjWFVk+YLtkusZ8bi72Zf1f6xOuQdJKEAEoqL16uwBdAVb9cgXa/eheSEgbUGtsWogrSFwGYtnZxJgWYJ6/z+bF96gsLUuaBYalONYcR0g1cj5/jYh2HNIlzd/2u0TecuQWIrJnrGy5oiq0szTgsByjlAbXl/AAsJrNCvABLrh5qdeUoWxjn4kRCpVZZ++yVpeO3hWTfjRw2+tM7GjFvyykpcq1ecgdZy+7uUrWbaDJFBtlKZOKNIgpjwEANTIhQiVQdGFTZIS83EHeOtzuHFoRqLgdwuXZzUzaC+Bc52cd1Hb0F654Za5cr+9nZgeLkJ5y9wcrA9hShJcPeK3PtUaHk1lxy0psIiGN5aJIK5fGAwCApLutoPky01Ia3AqmN8iou5mqxalJ6ysBLwQgp6Ire8pQBVQsoksTOhfQ39XCHRlfZvRKCHKv0Y++1NlMUlEboW4Zh+j92fMbNeOeEZEj0SklQpLbzx1cPWvlfqD1jev1D1u+Uoqmxq5k4cPr+6fmjB3Xvg6ciIjzvJsZYVv4vLltsrXjivWbCdyq0RAGpB5U5KVJ91FHRaydLN5TgkKdFpTBbXO/4v1Vpkbb4rXWadEqflze9+lt+xv2Y19v+CD01pb/qaJwBRA/buu1XusTrNG5cFmfOSeM1RglEVWmk7IhlbTk9jNR3kT2kRKViIJ+dc2xalu/dOQ3TAXKKmKXizMUTXv0HPrZbi5XAFi+qPH334BxM7rH00O+om9saQMKqFhZR5NiyQ8GK8pzlKW8hDAVYdmClW5ObWUWhbSliZkNExu31UwRoRr2W6Hrslb7/q8tRMSKkdfbtnrMlGRF56oPLutvhlB0y9Y6z1U5IAxUxOx9znOyC9uVfU8XmydKVdoJXUJsaXTrZERrgZAp8Kme1tkXq9ey4yWf1zSe9G19IT4kQ7UFbKXg3TVW+0Ent6te8Bc8I6/1Wn/YNVIHOI02Ms+fAcjcKxSMRlJSIYHARztLEmZI8xXN4khjsQCKNlVJru/MooDpmhBpNNLX9Ec0OrGGkKmT+oiGm3DxUrYM4GL17SzAZWJQc3clyVIQ+mma2Doq4Kr9an1vG7gsHmTHmozXkpR12j4JqwhR7NK9NLIcWHs0bELNe6S8wDHrSb/LL3b/QR9myYVWe3dRadcQlWrHs0WC6ou16vB1lSOveJQFJ2mZ9q3TWHkepaq454DN2HmZaiiufy2todX/1TN+88ReGc8Hfmdfr6wzpzgE5jxzmxJ+gM6u034lT+3vu50Czx95rdf6RGtogTzmrZ6mjJutaD+Nk15m1IBgV27TKs3Z06yolTSwE/wNkBFr7mAHjszk4FndRfB8W8k4o0h8WBAOWHNmVsXiKYtf9npZky1gJLBUrnu+fWMaeMYceFULV+aR3KYUt6mTVZE8L3/TbmPXJ7j2rvdxR6WlCzl5fvdznKwm7AICoiUiuJxTXsFV2i1SFAPVUVyBeQ+Tj2tU1lNClFF21o3TXLawWyUBlt0JeYpDspYQWZPIyrhjIYQdQXQOsUXhvw3E7+H/fofsTr1ZXTukht7/+mxtbUYdSN9L23n9CDS91mv94ddQcTSrX9dSDL5Cb8tgcYbsovTlQ2xCNMa7glNrmeIsuEU3eJacclvhQNpbQ0xUW+kV3F2ArC2DcxnuVTVFmwxWLPdDRk86eaaewQdEOLudSbMeJtMJxmWR17+FFJXhqzYIo5Fa+g1Al1PBrigDCbpvhok/7Gcbbm1fermrBEnWELROFK4UISJ6HEI5BXcrQCs9VgkvUJF19aqjZPZ1RejPgEkXai3L2nXERY2NtY3qeUY2gV8HkrgPFh0ru89Yn8OGv69vS3eVJY3NiV4+Y3fh65WImn9DfujbYCOEi8a2DuvDl2Pf5mu91qdaKQYXmbdnsp16BQQUKQ1Gd+PigoQigYEUUijQOcNkAI7VmCMs2ZkdwVcFzUFxx3BwRWoqTGPL3FtP/3rCSWaLr9TBece/naaUKVjWf4NHVBMQYDkbtjyQZ2h/wQ4X/BABa3G3CEOFzSFG7VU6knJQlbLwozgz0PayxpRfsFXawTnnE3ayZTCRhYKrBZhmuLCNOi2IqjtTMXUGzbOcA6UPyHNUVjg3X9ZTSfu3FRwnJ3OdisKHsmE4RbOZXKkWDy/Pc12munylUkqgZH6WXa5LWbuSTRvXWMpOSGYHJXuY39nhcmyZ08VyWntaUE4u/VDewHtR/RX+v9YnXFUERsIO9T8SDqDFHHMucGm2m2k9Vj8EZeuxt8QTtkR7C9vLZqfOWBmF7YEvhAGpIF1CY2qEJOPiBFtOdW9qlZF1tQE/FwA/WA3210VFum2DEpPqcmHEYo+CpNe5SR9mkBFRo9MApXJGokr24QS1BBwLquKC2nofpVLzR6lyXntbW9K22Ihck+KhcrdR+tK0pCMpwqBGu8vc50logTltXyQJUYBPXsL86M6h7DMqgtTWd23ulQBddQVKMi7mfhnfdTnW/XPtHMANe2znnch/hR752bgKIReAs6DKvLJrHmS/2TqJtQohXuu1PvEaWtU5ltpOQ71IcuSUGh4vhEdKUBiSLXQCF5JgmzVZDZ+xdEDLsCK1ZYJbWNcfTKMjVGGT/b0ru1eqekXhLljtx8ue5OjzHwNGlAnrCjSeTEAhFIswU75kiexfqp8hcdlEXNx+ZlheSUy50Ig6vcoBMA30b7sX7QVz5O9lFteOLQNqC603bIrQahtovcMiZYaaclybayirWLV1Yj/E412K2AG3hcIvH2BL2aElPgnaQnjayKZNNm2iPfsVrw/mKV6HbY4O8Ot6CagyEj7ccs/n6qourG67/Dli9nVcx/KCfV7rU6/RUpeplwKoBgK3tUkAg9KSGJtptSVDdWOt+GuFtMuaXHW2D5w/fKg6bsBO/kZaF4AbCEklzzVBZTNOZsZ2MP3AX0Fn/ssVJza8fFmW9lV13Or22iQFLRSLKNygQtsQaHQCYNcSav+fmhI274WVqdROILdZEevz+dlrnpUblcb1tdfa3sM+yUuKIlaCgpYIFZQ1FzNEH0Reh2bR5D7CFkO3J+f0GctKfrQkdGUS3YLHff8bLwJAc3tyuH2XoXGl68hRjnplHnn9qhyzXff9PKP8o3aS1X534enTr/Van31lI9gKlNoBUC322SZxBfXdupWPWEPY6hExVT5sHCbDPy4nkRHxko5EtyB04JlwUPGFIhLNhizttdEMMTN8i/1ZjqWOcLmgRunbJF+VCaEIIeQVCGcmFBnPF22prHYGzCGAwYrBSyhC0gyhdJ/zi1j+RBIj0ogaGRVZl8ry2vfNy2qLsnPHF/uo+KPPopVs4taqyuT2EuZScfjX90SoO3jzZGQdRDWAjIsnCuADRI6u0wqrRIFQrBSEjBVQL2tbvSBa9wk6GG8XmG0f3X6XUqComW5PdYLl8GMJSHSxYceRGvlpJ3+pwtXtu93MT9jgApFe67U+zxrLPKZemBIWTxtBmxGh2IksJHt4CDpUVBo31EtoBLZWFOU+tviY/bSqzQfbQ3RgHgJajWfDKMqf5Oc73kwa+vIlttQtrk/sviH9iSLkbsv651u3nRcJd08ZZLVxgSDICJrN88zEIGGZ9R3lylQ9xlk+qV4DLC25Ph3X0JUn+mliFGYWpflfctB5ONmo1YezSguUUiFVc549sSZlvdWaENdlSF2MHJKwy4Yuze315i1B4cJmul/AyiD3Nc+dT+j/aeDP1eb2wVujMSWsTAK7YvMl65TOEEZbAGDfeHvKVcnHypAaj8Lu5Nbab9fXeq3Ps8YyxPl7Pi0rkW9DJkAKmVnWh/uxigvW6I9ji7MWBT7ZijVzBaUqFAi3AmR/IMuTOT9LqxKw/oKreLwixM1arVheC2PY9m394m41vOwJhrqURDM6XZNNuDB9gNkWXSCTCEuIx3A1ksWqWAClZ1mYy3oLpZqgGHOCMLO9sNknkwvU7ubqiveXV6uzCjQKt2bI5Is1XaurDitFKJueRW+WzZUUgcBVDL98kiSU0BDqJ9ruJy7EZhtpqY7Zo1Sg669JS8q/q8epFXQVga46GK3qRTUVoc42m2iwWfmnKH5PLoFV6tjhoCf46LVe67OtJB3WStuW9c80K22vdtFEACkvwytwLkV7W2U3bM9VPWwQKm69Hrzdvnc9mSn9mJsyM3ffw7oM0j/m6q1qubQt2xVg54D2cZaxwbX/vZlqEeoEpcUbSuG4TVo7uOXhhJz71VA5MqV6Sixqr7t+cbkH9c4CcPcf49MPEkCXb75sLjLkTm8ihRUA1leBABRxZic2S2211B1CM3SGZnJH6xpuzdLr7HXCl01h7do3Lg2LZVvzgpY/Wxvp/EyVaSUChO1WKdnOQMl2FvK/LnRnf9jQraeq8ofz1vfpqo1fp26dwM0rv9ZrfaKVEFBcthGIDglD6Baq+i8HsKgx627KX8HX9RjvldWqcDb5/XrPRrHcbAQVynm6aV4/CMLkt5vZYmhmgsIPJUGt4uRTBWLtG8k5w83zW8r9VHVCtsXXc85qVG7HI0maS44Ua0BV+jkzKpOCyzT2ty+4Y5mep3E63IYkr0D1Nw3W9rrS29SLCXeLT8YNWmLR7eHy6HYifNQdwHbXO/LWycFKRwJXrNBXodzwzmLShu/1Vehp709pyp49PLGKU1tC+z1TxeALeMQPShjr1toO8Lrv913a3vDyAa/1udb/D/SaP54vbG28AAAAAElFTkSuQmCC",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=512x256>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = to_pil_image(data[\"image\"].clone())\n",
    "msk = to_pil_image(data[\"mask\"]).convert(\"RGB\")\n",
    "\n",
    "Image.fromarray(np.hstack((np.array(img), np.array(msk))))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "anomalib",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "ae223df28f60859a2f400fae8b3a1034248e0a469f5599fd9a89c32908ed7a84"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
